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Career Path - Artificial Intelligence & Machine Learning Engineer

Learn programming, data science, computer sc, math, statistics, problem-solving, machine learning, deep learning to become a Machine Learning engineer
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Course Duration: 220 Hours
Preview Career Path - Artificial Intelligence & Machine Learning Engineer course
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Courses included in Artificial Intelligence & Machine Learning Engineer Career Path Program are the following:

 

1).Python Programming

2).Python Programming (basic to advanced)

3).R Programming (basic to advanced)

4).Data Visualization with Python

5).Data Visualization with R

6).Machine Learning (basic to advanced)

7).Machine Learning with Python

8).Deep Learning Foundation

9).Deep Learning with Keras

10).Deep Learning with TensorFlow

11).Interview Questions - Machine Learning

12).Interview Questions - TensorFlow

 

Machine learning (ML) is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Artificial Intelligence (AI) is a computer system’s ability to mimic human behavior. Machines demonstrate this sort of intelligence, which can be compared to a natural intelligence that humans and animals demonstrate. An AI engineer builds AI models using machine learning algorithms and deep learning neural networks to draw business insights, which can be used to make business decisions that affect the entire organization. These engineers also create weak or strong AIs, depending on what goals they want to achieve. 

 

At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data. Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video.

As an AI engineer, you need to perform certain tasks, such as develop, test, and deploy AI models through programming algorithms like random forest, logistic regression, linear regression, and so on. An Artificial Intelligence (AI) architect develops, manages and oversees AI initiatives within an organization. An AI architect should have deep knowledge in mathematics and statistics. Professionals who are finding how to become an AI engineer should also know about the skills required in this field. Some of them include: Programming Skills, Linear Algebra, Probability, and Statistics, Spark and Big Data Technologies, Algorithms and Frameworks, Communication and Problem-solving Skills.

 

This Machine Learning Career Path program by Uplatz will help you acquire skills necessary to become a successful Machine Learning / Deep Learning Engineer. This can be a game-changer for your career and can put you on track for a highly-paid lucrative and challenging career in cutting-edge technology.

 

Course/Topic 1 - Python Programming - all lectures

  • In this lecture session we learn about introduction to python programming for beginners and also talk about features of python programming.

    • 10:21
  • In this lecture session we learn about basic elements of python in python programming and also talk about features of elements of python.

    • 19:37
  • In this lecture session we learn about installation of python in your system and also talk about the best way of installation of python for beginners.

    • 13:18
  • In this lecture session we learn about input and output statements in python programming and also talk about features of input and output statements.

    • 24:05
  • In this lecture session we learn about data types in python programming and also talk about all the data types in python programming.

    • 23:05
  • In this lecture session we learn about operators in python and also talk about how we use operators in python programming.

    • 47:07
  • In this lecture session we learn about different types of operators in python programming and also talk about features of operators in python.

    • 29:47
  • In this lecture session we learn about type conversion in python programming and also talk about features of type conversion in python.

    • 23:39
  • In this lecture session we learn about basic programming in python programming for beginners.

    • 15:56
  • In this lecture session we learn about features of basic programming in python and also talk about the importance of programming in python.

    • 05:13
  • In this lecture session we learn about math modules in python programming and also talk about features of math modules in python.

    • 26:43
  • In this lecture session we learn about conditional statements in python and also talk about conditional statements in python programming.

    • 28:24
  • In this lecture session we talk about basic examples of conditional statements in python.

    • 19:27
  • In this lecture session we learn about greater and less then conditional statements in python programming.

    • 13:39
  • In this lecture session we learn about nested IF Else statements and also talk about features of nested IF else statements.

    • 11:04
  • In this lecture session we learn about looping in python in programming for beginners and also talk about looping in python.

    • 25:06
  • In this lecture session we learn about break and continue keywords and also talk about features of break continue keywords.

    • 20:48
  • In this lecture session we learn about prime number programs in python and also talk about functions of prime number programs in python.

    • 17:31
  • In this lecture session we learn about while loop in python programming and also talk about features of while loop in python.

    • 35:35
  • In this lecture session we learn about nested For loop in python programming and also talk about features of nested For loop.

    • 12:34
  • In this lecture session we learn about features of nested for loop in python and also talk about the importance of nested For loop in python.

    • 12:49
  • In this lecture session we learn about functions in python and also talk about different types of functions in pythons.

    • 19:28
  • In this lecture session we learn about passing arguments to functions in python programming and also talk about features of passing arguments to functions

    • 08:59
  • In this lecture session we learn about return keywords in python and also talk about features of return keywords in python.

    • 12:16
  • In this lecture session we learn about calling a function in python programming and also talk about calling a function.

    • 15:07
  • In this lecture session we learn about factors of calling a function in python programming and also talk about features of calling a function.

    • 20:17
  • In this lecture session we learn about a program to swap 2 numbers using calling a function in python programming.

    • 19:27
  • In this lecture session we learn about functions of arbitrary arguments in python programming and also talk about features of arbitrary arguments.

    • 10:34
  • In this lecture session we learn about functions keywords arguments in python programming and also talk about features of keyword arguments.

    • 06:55
  • In this lecture session we learn about functions default arguments in python programming and also talk about features of default argument.

    • 06:57
  • In this lecture session we learn about global and local variables in python programming and also talk about features of global and local variables.

    • 19:37
  • In this lecture session we learn about global and local keywords and also talk about features of global and local keywords.

    • 10:44
  • In this lecture session we learn about strings in python programming and also talk about features of string in python.

    • 17:42
  • In this lecture session we learn about string methods in python programming and also talk about features of string methods in python.

    • 21:53
  • In this lecture session we learn about string functions in python and also talk about features of strings functions in python.

    • 28:02
  • In this lecture session we learn about string indexing in python programming and also talk about features of string indexing in python programming.

    • 13:51
  • In this lecture session we learn about introduction of lists in python programming and also talk about features of introduction to lists.

    • 06:31
  • In this lecture session we learn about basics of lists python programming and also talk about features of basics of lists in python.

    • 33:09
  • In this lecture session we learn about list methods and also talk about features of list method python programming.

    • 32:43
  • In this lecture session we learn about linear search on list and also talk about features of linear search on list in brief.

    • 23:20
  • In this lecture session we learn about the biggest and smallest number of the list and also talk about features of MAX and Min in a list.

    • 14:40
  • In this lecture session we learn about the difference between 2 lists in python programming and also talk about features of 2 lists.

    • 13:22
  • In this lecture session we learn about tuples in python programming and also talk about tuples in python programming.

    • 20:19
  • In this lecture session we learn about introduction to sets in python and also talk about functions of introduction to sets in python.

    • 32:43
  • In this lecture session we learn about set operations in python programming and also talk about features of set operation in brief.

    • 26:56
  • In this lecture session we learn about set examples and also talk about features set examples.

    • 11:05
  • In this lecture session we learn about introduction to dictionaries in python programming and also talk about featured dictionaries.

    • 14:47
  • In this lecture session we learn about creating and updating dictionaries in python programming and also talk about features of creating and updating dictionaries.

    • 32:49
  • In this lecture session we learn about deleting items in a dictionary in python programming and also talk about features of deleting items in a dictionary.

    • 08:06
  • In this lecture session we learn about values and items in a dictionary in python programming and also talk about features of values and items in the dictionary.

    • 13:14
  • In this lecture session we learn about dictionary methods in python programming and also talk about features of dictionary methods.

    • 18:46
  • In this lecture session we learn about built in methods in python programming and also talk about features of built in methods in python.

    • 20:25
  • In this lecture session we learn about lambda functions and also talk about features of lambda function in python programming.

    • 15:29
  • In this lecture session we learn about file handling in python programming and also also talk about the importance of file handling in python.

    • 15:58
  • In this lecture session we learn about file handling in python programming and also talk about features of file handling in python.

    • 36:13
  • In this lecture session we learn about exception handling in python and also talk about features of exception handling in python.

    • 08:46
  • In this lecture session we learn about exception handling examples in python programming.

    • 25:04
  • In this lecture session we learn about python programs in python programming and also talk about features of python programs

    • 18:40
  • In this lecture session we learn about the program of printing odd numbers in python programming and also talk about the best way of printing.

    • 10:46
  • In this lecture session we learn about counting the number of vowels and consonants in a string and also talk about features of these programs.

    • 21:38
  • In this lecture session we learn about python programs of swapping two numbers in a list by taking indexes as parameters.

    • 14:08
  • In this lecture session we learn about bubble sort and also talk about features of bubble sort in brief.

    • 35:36
  • In this lecture session we learn about operator precedence in python and also talk about features of operator precedence in python.

    • 14:51
  • In this lecture session we learn about operator precedence in python and also talk about features of operator precedence types.

    • 11:28
  • In this lecture session we learn about recursion in python and also talk about features of recursion in python.

    • 22:15
  • In this lecture session we learn about binary search in python and also talk about features of binary search in python programming.

    • 23:18
  • In this lecture session we learn about binary search in python and also talk about the importance of binary search in python.

    • 35:04
  • In this lecture session we learn about object oriented programming and also talk about features of object oriented programming in brief.

    • 21:52
  • In this lecture session we learn about factors and types of object oriented programming in python programming.

    • 17:41
  • In this lecture session we learn about OOPS and procedural programming and also talk about features of OOPS and procedural programming in OOPS.

    • 06:36
  • In this lecture session we learn about OOPS programs in python and also talk about the importance of OOPS.

    • 27:50
  • In this lecture session we learn about inheritance in python programming and also talk about features of inheritance.

    • 37:24
  • In these lecture sessions we learn about features of object creation in python programming and also talk about object creation in python.

    • 24:10
  • In this lecture session we learn about OOPS terminology and functions and also talk about features of OOPS terminology and functions.

    • 24:41
  • In this lecture session we learn about built in class attributes and garbage collection in python programming.

    • 27:26
  • In this lecture session we learn about inheritance in python and also talk about features of inheritance in python.

    • 19:02
  • In this lecture session we learn about the importance of inheritance in python programming and also talk about functions of inheritance.

    • 29:26
  • In this lecture session we learn about programs in inheritance in python programming and also talk about features of inheritance in python.

    • 31:43
  • In this lecture session we learn about polymorphism in python programming polymorphism and also talk about polymorphism in python.

    • 24:47
  • In this lecture session we learn about features of polymorphism in python and also talk about the importance of polymorphism in python.

    • 14:01
  • In this lecture session we learn about the time module in python and also talk about features time module in python in features.

    • 36:22
  • In this lecture session we learn about the importance of time modules in python time module in python in brief.

    • 44:51
  • In this lecture session we learn about the calendar module in python programming in brief.

    • 32:04
  • In these lecture sessions we learn about calendar methods in python programming and also talk about the importance of calendar methods.

    • 37:03
  • Class 28.1 - Boolean in Python

    • 09:32
  • In this lecture session we learn about python iterators and also talk about features of python iterators in brief.

    • 09:30
  • In this lecture session we learn about python programs and summary in python programming and also talk about python programs.

    • 46:37
  • In this lecture sessions we learn about python programs and also talk about features of python programs and summary.

    • 23:27

Course/Topic 2 - Python Programming (basic to advanced) - all lectures

  • This video comprehends the terms Python which is to develop by Guido van Rossum. Guido van Rossum started implementing Python in 1989. Python is a very simple programming language so even if you are new to programming, you can learn python without facing any issues.

    • 21:04
  • This topic will cover, Installing Python which is generally easy, and nowadays many Linux and UNIX distributions include a recent Python. Even some Windows computers now come with Python already installed.

    • 34:34
  • In this Python tutorial, we will learn about Python variables and data types which is being used in Python. We will also learn about converting one data type to another in Python and local and global variables in Python. So, let’s begin with Python variables and data types Tutorial.

    • 30:53
  • In this topic you will learn about the data type which is an important concept. Variables can store data of different types, and different types can do different things.

    • 45:15
  • This session will teach you about the Python defines type conversion functions to directly convert one data type to another which is useful in day to day and competitive programming. This article is aimed at providing information about certain conversion functions.

    • 31:11
  • In this tutorial, you will learn about the keywords which is the reserved words in Python and identifiers names given to variables, functions, etc. We cannot use a keyword as a variable name, function name or any other identifier. They are used to define the syntax and structure of the Python language.

    • 40:45
  • In this tutorial, we are going to learn how to take multiple inputs from the user in Python. The data entered by the user will be in the string format. So, we can use the split method to divide the user entered data.

    • 33:57
  • This tutorial focuses on two built in functions print and input to perform Input and Output task in Python. Also, you will learn to import modules and use them in your program. Some of the functions like input and print are widely used for standard input and output operations respectively. Let us see the output section first.

    • 54:08
  • This tutorial covers the different types of operators in Python, operator overloading, precedence and associativity. Just like in mathematics, programming languages like Python have operators. You can think of them as extremely simple functions that lie at the basis of computer science.

    • 35:15
  • In this tutorial, you'll learn everything about different types of operators in Python, their syntax and how to use them with examples. Operators are special symbols in Python that carry out arithmetic or the logical computation. The value that the operator operates on is called the operand.

    • 31:05
  • Previously, in our tutorial on Python Operators., Today, in this Python Bitwise Operators Tutorial, we will discuss Python Bitwise AND, OR, XOR, Left-shift, Right-shift, and 1’s complement Bitwise Operators in Python Programming. Along with this, we will discuss syntax and example of Python Bitwise Operators.

    • 44:28
  • Today, we talk about Python decision making constructs. This includes Python if statements, if else statements, elif statement, nested if conditions and single statement conditions. We will understand these with syntax and example to get a clear understanding. So, let’s start the Python Decision Making Tutorial.

    • 37:10
  • In this session you will learn about the if elif else which are conditional statements that provide you with the decision making that is required when you want to execute code based on a particular condition. The if elif else statement used in Python helps automate that decision making process.

    • 30:46
  • In this session, you'll learn the different variations of for loop, for loop is used for iterating over a sequence that is either a list, a tuple, a dictionary, a set, or a string. This is less like the for keyword in other programming languages, and works more like an iterator method as found in other object-orientated programming languages.

    • 37:25
  • In this session, you will learn to create a while loop in Python. Loops are used in programming to repeat a specific block of code. In this article, you will learn to create a while loop in Python. Loops are used in programming to repeat a specific block of code.

    • 38:54
  • In this session, we show how to create an infinite loop in Python. An infinite loop that never ends it never breaks out of the loop. So, whatever is in the loop gets executed forever, unless the program is terminated. For certain situations, an infinite loop may be necessary.

    • 38:57
  • In this video, you will learn how to make the computer execute a group of statements over and over if certain criterion holds. The group of statements being executed repeatedly is called a loop.

    • 29:04
  • In this session, you'll learn about the different numbers used in Python, how to convert from one data type to the other, and the mathematical operations supported in Python. Python supports integers, floats and complex numbers.

    • 43:57
  • In the tutorial on strings in Python, you learned how to define strings objects that contain sequences of character data. Processing character data is integral to programming. It is a rare application that doesn’t need to manipulate strings at least to some extent.

    • 23:30
  • As discussed in the above tutorial, strings in Python are immutable and thus updating or deleting an individual character in a string is not allowed, which means that changing a particular character in a string is not supported in Python. Although, the whole string can be updated and deleted.

    • 55:48
  • In this session, we'll learn everything about Python lists, how they are created, slicing of a list, adding or removing elements from them and so on. The list is a most versatile datatype available in Python which can be written as a list of comma-separated values items between square brackets. Important thing about a list is that items in a list need not be of the same type.

    • 44:43
  • In this tutorial, learn how to update list element using Python. Use the index position and assign the new element to change any element of List. You can change the element of the list or item of the list with the methods given here.

    • 39:20
  • In that tutorial of Python Functions, we discussed user-defined functions in Python. But that isn’t all, a list of Python built-in functions that we can toy around with. In this tutorial on Built-in functions in Python, we will see each of those, we have 67 of those in Python 3.6 with their Python Syntax and examples.

    • 24:00
  • In this tutorial, you'll learn everything about Python tuples. More specifically, what are tuples, how to create them, when to use them and various methods you should be familiar with. A tuple in Python is similar to a list. The difference between the two is that we cannot change the elements of a tuple once it is assigned whereas we can change the elements of a list.

    • 34:37
  • This session teaches you the tuple in Python which are immutable sequences, you cannot update them. You cannot add, change, remove items (elements) in tuples.Tuple represent data that you don't need to update, so you should use list rather than tuple if you need to update it. However, if you really need to update tuple, you can convert it to list, update it, and then turn it back into tuple.

    • 39:57
  • In this tutorial, you'll learn everything about Python dictionaries how they are created, accessing, adding, removing elements from them and various built in methods. Python dictionary is an unordered collection of items. Each item of a dictionary has a pair. Dictionaries are optimized to retrieve values when the key is known.

    • 52:22
  • In this session we will teach you the dictionary which is a data type similar to arrays, but works with keys and values instead of indexes. Each value stored in a dictionary can be accessed using a key, which is any type of object a string, a number, a list, etc. instead of using its index to address it.

    • 34:32
  • In this session we will teach you the Python for beginners training course which is a lead the students from the basics of writing and running Python scripts to more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting.

    • 38:11
  • In this video, you will learn to manipulate date and time in Python with the help of examples. Python has a module named datetime to work with dates and times. Let's create a few simple programs related to date and time before we dig deeper.

    • 26:48
  • In this session, you'll learn about functions, what a function is, the syntax, components, and types of functions. Also, you'll learn to create a function in Python. In Python, a function is a group of related statements that performs a specific task. Functions help break our program into smaller and modular chunks.

    • 42:07
  • In this video we will learn, the function which use the same variable and object. Pass by Value. In pass by value the function is provided with a copy of the argument object passed to it by the caller. That means the original object stays intact and all changes made are to a copy of the same and stored at different memory locations.

    • 28:54
  • In this tutorial, you'll learn about the anonymous function, also known as lambda functions. You'll learn what they are, their syntax and how to use them with examples.

    • 31:23
  • In this tutorial we will teach you the module which is a piece of software that has a specific functionality. Like, when building a ping pong game, one module would be responsible for the game logic, and another module would be responsible for drawing the game on the screen. Each module is a different file, which can be edited separately.

    • 31:39
  • This session teaches you the Python rename method which is used to rename a file or directory. This method is a part of the python module and comes extremely handy.

    • 37:02
  • In this tutorial, you'll learn about Python file operations. More specifically, opening a file, reading from it, writing into it, closing it, and various file methods that you should be aware of.

    • 34:35
  • In this tutorial we will learn about program for files in Python which provides us with an important feature for reading data from the file and writing data into a file. Mostly, in programming languages, all the values or data are stored in some variables which are volatile in nature.

    • 36:20
  • In this session we will tell you the method that you the current position within the file; in other words, the next read or write will occur at that many bytes from the beginning of the file. The seek method changes the current file position.

    • 38:05
  • In this tutorial, you'll learn how to handle exceptions in your Python program using try, except and finally statements with the help of examples. Python has many built-in exceptions that are raised when your program encounters an error (something in the program goes wrong).

    • 38:08
  • In this tutorial, you will learn about different types of errors and exceptions that are built-in to Python. They are raised whenever the Python interpreter encounters errors.

    • 42:46
  • In this video we will teach you about the Exception handling in Python which is very similar to Java. The code, which harbors the risk of an exception, is embedded in a try block.

    • 31:27
  • In this tutorial, you will learn about the core functionality of Python objects and classes. You'll learn what a class is, how to create it and use it in your program.

    • 38:34
  • In this session you will learn about the programming in Python (object-oriented programming) for some time, then you have definitely come across methods that have self as their first parameter. Let us first try to understand what this recurring self-parameter is.

    • 42:03
  • This tutorial teaches you about the regular expression which is a special sequence of characters that helps you match or find other strings or sets of strings, using a specialized syntax held in a pattern. Regular expressions are widely used in UNIX world.

    • 46:36
  • In this tutorial we will learn about the python search which is a method of the module That is Syntax of search () re. search (pattern, string). It is similar to re. match () but it doesn’t limit us to find matches at the beginning of the string only. Unlike in re. match () method, here searching for pattern ‘Tutorials’ in the string ‘TP Tutorials Point TP’ will return a match.

    • 53:36
  • This workshop will introduce GUI programming in Python, it is a is a popular language for elementary programming but it not so easy to write programs with a graphical user interface (GUI).

    • 28:07
  • In this tutorial, we will learn how to develop graphical user interfaces by writing some Python GUI examples using the Tkinter package. Tkinter package is shipped with Python as a standard package, so we don’t need to install anything to use it.

    • 41:04
  • This session teaches you about the frame widgets which is a rectangular region on the screen. The frame widget is mainly used as a geometry master for other widgets, or to provide padding between other widgets.

    • 46:10

Course/Topic 3 - R Programming (basic to advanced) - all lectures

  • In this lecture session we learn about basic introduction to R programming and also talk about some key features of R programming.

    • 16:58
  • In this lecture session we learn about the setup of R language in your system and also talk about the importance of R programming.

    • 24:13
  • In this lecture session we learn about variables and data types in R language and also talk about types of variables and data types in R programming.

    • 31:36
  • In this lecture session we learn about uses of variable and data types in our programs and also talk about some key features of variables and data types.

    • 29:16
  • In this lecture session we learn about input - output features and also talk about features of input - output features.

    • 38:05
  • In this lecture session we learn about posted function () in input output features and also talk about features of posted functions().

    • 25:43
  • In this lecture session we learn about operators in R and also talk about features of operators in R programming.

    • 34:16
  • In this lecture session we learn about different types of operators in R language and also talk about features of all types of operators in R.

    • 29:19
  • In this lecture session we learn about vectors in data structure in R programming and also talk about features of vectors in data structures in brief.

    • 32:29
  • In this lecture session we learn about the importance of vectors in data structure and also talk about vectors in data structures in brief.

    • 30:29
  • In this lecture session we learn about list data structure in R programming and also talk about features of list in data structure.

    • 33:47
  • In this lecture session we learn about more operations on the list and also talk about features of List in data structures in brief.

    • 29:10
  • In this lecture session we learn about matrix in R programming and also talk about features of matrix in data structure in R language.

    • 43:57
  • In this lecture session we learn about matrix in R programming and also talk about features of matrix in data structure in R language.

    • 25:15
  • In this lecture session we learn about matrix data structure in R programming and also talk about some key features of matrix and data structure.

    • 09:36
  • In this lecture session we learn about arrays in R programming and also talk about features of arrays.

    • 31:56
  • In this lecture session we learn about different types of arrays in data structure and also talk about features of Arrays in data structure in brief.

    • 38:35
  • In this lecture session we learn about data frame in R programming and also talk about function of data frame.

    • 43:06
  • In this lecture session we learn about data frame features in R programming and also talk about the importance of data structure.

    • 33:59
  • In this lecture session we learn about the importance of Data frame in brief and also talk about function of data frame in R programming.

    • 31:20
  • In this lecture session we learn about data frame key features of data frame in data structure in brief.

    • 19:40
  • In this lecture session we learn about factors data structures in R programming and also talk about the importance of factors.

    • 34:09
  • In this lecture session we learn about factors of data structure in R programming and also talk about different types of factors in R language.

    • 17:14
  • In this lecture session we learn about decision making in R programming and also talk about features of decision making in R.

    • 32:52
  • In this lecture session we learn about different types of decision making statements and also talk about features of all decision statements.

    • 15:53
  • In this lecture session we learn about decision making using integers and also talk about functions of integers.

    • 30:02
  • In this lecture session we learn about Loops in R programming and also talk about factors of Loops in R language.

    • 28:28
  • In this lecture session we learn about functions of Loops and why we need Loop statement in R programming and also talk about key features of Loop statement.

    • 32:03
  • In this lecture session we learn about different types of Loops in R programming and also talk about features of For loop, while loop and do while loop.

    • 25:29
  • In this lecture session we learn about functions in R programming language and also talk about features of functions in R language.

    • 37:42
  • In this lecture session we learn about different types of functions in R programming and also talk about the importance of functions.

    • 34:11
  • In this lecture session we learn about string in R programming and also talk about features of string function in R.

    • 25:18
  • In this lecture session we learn about why we need strings in R programming and also talk about the importance of strings.

    • 26:51
  • In this lecture session we learn about packages in R programming and also talk about features of packages in R.

    • 35:17
  • In this lecture session we learn about data and file management in R programming and also talk about functions of data and file management.

    • 32:45
  • In this lecture session we learn about how we manage the data and file in R programming and also talk about the importance of data and file management.

    • 23:18
  • In this lecture session we learn about Line chart in R programming and also talk about features of line chart in brief.

    • 33:06
  • In this lecture session we learn about scatterplot in R language and also talk about functions of scatters plot.

    • 26:38
  • In this lecture session we learn about Pie chart in R programming and also talk about features of Pie Chart in brief.

    • 33:32
  • In this lecture session we learn about bar charts in R language and also talk about features of Bar chart in brief.

    • 09:43
  • In this lecture session we learn about how we use bar charts in R programming and also talk about features of Bar charts.

    • 29:50
  • In this lecture session we learn about histogram in R programming and also talk about features of histogram.

    • 26:34
  • In this lecture session we learn about Boxplots in R programming and also talk about features of Boxplot in R language.

    • 21:49

Course/Topic 4 - Data Visualization in Python - all lectures

  • In this first video tutorial on Data Visualization in Python course, you will get a brief introduction and overview on what is data visualization, its importance, benefits and the top python libraries for Data Visualization like Matplotlib, Plotly and Seaborn.

    • 25:25
  • In this first part of the video on Matplotlib, you will learn both the theoretical and the practical knowledge on Matplotlib, which is one of the most popular and top python libraries for Data Visualization. You will get a complete introduction to Matplotlib, the installation of Matplotlib with pip, the basic plotting with Matplotlib and the Plotting of two or more lines in the same plot.

    • 30:44
  • In this second part of the Matplotlib video tutorial, you will learn how to add labels and titles like plt.xlabel and plt.ylabel along with understanding how to create lists and insert functions onto it. All this can be seen explained it detail by the instructor by taking examples for it.

    • 21:14
  • In this tutorial, you will learn about 2 important python libraries namely; Numpy and Pandas. Along with the theoretical concepts, you will also get practical implementation on various topics related to these two such as what is Numpy and what is its use, the installation of Numpy along with example, what is pandas and its key features, with the installation of Python Pandas and finally the Data Structure with examples of Pandas.

    • 54:10
  • In this second part of the Numpy and Pandas tutorial, you will learn the complete overview of Pandas like its history, its key features, the installation process of Pandas, Pandas Data Structure and within it the Data Frame and syntax to create Data Frame. All this will be explained in detail by the instructor.

    • 41:10
  • In this third part of the video tutorial on Numpy and Pandas, you will learn about creating Data Frame from Dictionary. Also, you will understand how to read CSV Files with Pandas using practical examples by the Instructor.

    • 30:19
  • In this tutorial, you will learn about the different Data Visualization Tools such as Bar Chart, Histogram and the Pie Chart. You will get a complete understanding of what is these tools, why and how to use these 3 tools, the syntax for creating Bar Chart, Histogram and the Pie Chart and different programs for creating these data visualization tools. In the first part of the video, you will learn about the Bar Chart and in the subsequent videos, you will learn about the Histogram and the Pie Chart.

    • 49:49
  • In this second part of the Data Visualization Tools video, you will learn about the complete overview of Histogram like what is Histogram, how to create Histogram and many others with the help of practical examples by the instructor.

    • 37:01
  • In this third and final part of the Data Visualization Tools video, you will learn about the Pie Chart-what is Pie Chart, how to create the Pie Chart and how to create the syntax for Pie Chart? All these questions will be explained in detail by the instructor by taking practical examples. Further, you will understand the concept of Autoptic parameter in Pie Chart.

    • 47:31
  • In this first part of the video tutorial on more data visualization tools, you will learn about some additional data visualization tools apart from Bar Chart, Histogram and Pie Chart such as Scatter Plot, Area Plot, STACKED Area Plot and the Box Plot. The first part of this tutorial consists of mainly the Scatter Plot, the theoretical concepts associated with it such as what is Scatter Plot, the syntax for creating Scatter Plot and creating Scatter Plot with examples.

    • 35:33
  • In this second part of the video tutorial, you will learn and understand what is Area Plot, creating Area Plot with Function and Syntax and creating Area Plot with examples. All these will be seen explained in detail by the instructor. Further, you will also learn and understand the concept associated with the STACKED Area Plot.

    • 40:32
  • In this final part of the video tutorial, you will learn about the Box Plot; which is also known as Whisker Plot, how to create Box Plot, its syntax and arguments used like Data & Notch, the parameters used in Box Plot such as vert, patch artist and widths. These will be seen explained in detail by the instructor.

    • 36:46
  • In this first video tutorial on Advanced Data Visualization Tools, you will learn about the Waffle Chart – its definition, complete overview, the syntax and programs to create Waffle Chart and the step-by-step procedure to create the Waffle Chart. All these will be seen explained in detail by the instructor.

    • 33:42
  • In this second part of the video tutorial on Advanced Data Visualization Tools, you will learn about the Word Cloud-its definition, the reason why Word Cloud is used, what are the modules needed in generating the Word Cloud in Python, how to install Word Cloud and how to create Word Cloud with the help of some examples.

    • 58:41
  • In this tutorial, you will learn and understand about the concept of Heat Map and how one can create the Heat Map along with the help of the parameter camps. This will be seen explained in detail by the instructor.

    • 37:45
  • In this first part of the video tutorial on Specialized Data Visualization Tools, you will learn about the Bubble Chart; its definition and how to create bubble charts with the help of different examples.

    • 35:55
  • In this video, you will learn about the Contour Plots; which is also sometimes referred to as Level Plots. Along with understanding the whole theoretical concept of Contour Plots, you will also learn how to create Contour Plots with practical examples as will be seen explaining by the instructor in details.

    • 32:04
  • In this third part of the video on Specialized Data Visualization Tools, you will learn about the Quiver Plot and how to create the Quiver Plot by taking different examples. This will be seen explained in complete details by the instructor.

    • 40:49
  • In this video on Specialized Data Visualization Tools, you will learn about 3D plotting in Matplotlib and also the 3D Line Plot used in Data Visualization with the help of different practical examples and how to create it. This will be seen explained in detailed by the instructor throughout the tutorial.

    • 41:41
  • In this tutorial, you will learn about the 3D Scatter Plot and how to create a 3D Scatter Plot. The instructor will be seen explaining this in complete details with the help of different examples.

    • 27:58
  • In this tutorial, you will learn and understand the 3D Contour Plot, what is the function used in creating the 3D Contour Plot and how it can be created; which will be explained in detail by the instructor with the help of examples.

    • 30:18
  • In this last part of the video tutorial on Specialized Data Visualization Tools, you will learn about the 3D Wireframe Plot and the 3D Surface Plot, along with creating the same with the help of different examples, seen explained in detail by the instructor.

    • 43:06
  • In this tutorial, you will learn about Seaborn, which is another very important Python library. Through this video, you will get an introduction to Seaborn, along with some important features of it, functionalities of Seaborn, Installation of Seaborn, the different categories of plot in Seaborn and some basic type of plots one can create using Seaborn like Distribution Plot.

    • 50:52
  • In this second part of the video on Seaborn Library, you will learn and understand some basic plots using Seaborn Library like the Line Plot. Here, the instructor will be seen explaining in detail the Seaborn Line Plot and with a detailed example of how to create Seaborn Line Plot with random data.

    • 24:38
  • This is a continuation video of creating the Line Plot with some more examples using the Seaborn library. Along with this, you will also learn about the Lmplot and the function used for creating the Lmport. This can be seen explained in detailed by the instructor with practical examples.

    • 23:34
  • In this tutorial, you will learn about Data Visualization using Seaborn library. Under this, you will learn the Strip Plot, how to create the strip plot and the program used to create the Strip Plot. This will be shown by the Instructor with detailed examples like Strip plot using inbuilt data-set given in Seaborn and others.

    • 31:37
  • In this video, you will learn about the Swarm Plot; its definition, complete overview and how you can create the Swarm Plot. This can be seen explained in detail by the instructor with examples like visualization of “fmri” dataset using swarm plot().

    • 30:05
  • In this tutorial, you will learn a complete overview on Plotting Bivariate Distribution along with the concepts of Hexbin Plot, Kernel Density Estimation (KDE) and the Reg Plots. You will understand many of the in-depth concepts on these, with detailed explanation by the instructor with examples.

    • 43:18
  • In this tutorial, you will learn about the Pair Plot Function in Visualizing Pairwise Relationship under Seaborn library. You will understand the complete overview of Pair Plot Function, the syntax for using it, the parameters used like hue, palette, kind and diag kind. This will be seen explained in detail by the instructor with the help of examples.

    • 34:31
  • In this tutorial, you will learn about the Box Plot, Violin Plots and the Point Plots – their definitions and how to create them which will be seen explained in detail by the instructor throughout the video.

    • 45:53

Course/Topic 5 - Data Visualization in R - all lectures

  • In this introductory tutorial on Data Visualization in R Programming, you will learn about what is data visualization, the type of graph or chart one should select for data visualization, what is the importance and benefits of data visualization and finally what are the applications of data visualization.

    • 28:24
  • In this video, you will learn how to work on the Histogram, which falls under different Chart types used in Data Visualization in R Programming; along with working on the bar chart, box plot and heat map. You will be seeing a detailed explanation by the instructor on the complete workaround of these by taking different examples.

    • 42:00
  • In this video, you will learn what is density plot and how you can create the density plot by taking different examples for it. You will also learn about the different applications being used in the density plot under Data Visualization with R Programming.

    • 17:28
  • In this tutorial, you will learn about Data Visualization with GGPLOT2 Package where inside it you will learn the overview of GGPLOT2, iteratively building plots, univariate distributions and bar plot, annotation with GGPLOT2, axis manipulation and the density plot. You will get a complete understanding of the theoretical concept along with the implementation of each of these.

    • 41:07
  • In this second part of the video tutorial, you will learn about Plotting with GGPLOT2 and building your plots iteratively, along with the importance of the ‘+’ symbol and its use in the GGPLOT2 work process. You will be seeing a detailed explanation from the instructor by taking different examples.

    • 27:40
  • In this video you will learn about the complete theoretical and practical implementation of Univariate Distribution and Bar Plot, which can be seen explained in complete details by the instructor throughout the tutorial.

    • 30:42
  • In this tutorial, you will learn about annotation with ggplot2, along with geom text () and adding labels with geom label () with complete explanation on this by the instructor with the help of different examples.

    • 50:28
  • In this tutorial, you will learn about Axis Manipulation with ggplot2, its complete overview and in-depth concepts along with the different functions used during the process. You will be seeing explaining the topic in complete details by the instructor by taking examples and working in R studio.

    • 34:13
  • In this section, you will learn about Text Mining and Word Cloud, along with the Radar Chart, Waffle Chart, Area Chart and the Correlogram. In this first part of the video, you will learn about the Text Mining and Word Cloud, the different reasons behind using Word Cloud for text data, who is using Word Clouds and the various steps involved in creating word clouds.

    • 31:43
  • In this video, you will learn how to execute data using redline function. Also, you will understand the usage of corpus function and content transformer function. Further, you will understand about the text stemming, Term Document Matrix function and the Max word’s function.

    • 31:49
  • In this tutorial, you will learn about the Radar Chart, the function used in the Radar Chart which is gg Radar (), scales, mapping and the use label. Along with this, you will also learn how to create Radar Chart in R studio. Moreover, you will learn about the Waffle Chart in R and how to create vector data in Waffle Chart with the help of different examples.

    • 34:18
  • In this last part of the session, you will learn about the Area Chart, its in-depth concepts and how to work on it. This will be seen explained in detail by the instructor. Moreover, you will also learn about the Correlogram in R, the correlation matrix, Mt cars and the work around on different visualization methods been used.

    • 39:54
  • This is a project tutorial titled Visualizing COVID-19 where you will see the different scenarios being explained by the tutor on visualizing COVID-19 data and how it can be done through Data Visualization in R process. In this first part, you will understand the complete overview of the project, its description and the different tasks associated with it being done by the ggplot.

    • 34:59
  • In this second part of the project video, you will learn about the “Annotate” process and the number of COVID cases being reported in China with the help of Data Visualization. You will be seeing the task performed on the dataset being provided by the WHO along with understanding the tribble function and how it will help during the entire work process.

    • 37:38
  • In this last part of the session, you will understand the work around of the task being done with the help of plot. You will see a detailed explanation by the instructor seeking help of few examples to explain the complete process of plotting in respect to the COVID-19 project being implemented.

    • 34:50

Course/Topic 6 - Machine Learning (basic to advanced) - all lectures

  • In this session we will learn about introduction to Machine Learning. We will start by learning about the basics of Linear Algebra required to learn Machine Language. Further we will learn about Linear equations represented by Matrices and Vectors.

    • 32:51
  • In this module we will learn about the computational roots of matrices. We will learn how to multiply matrix with scalar and vector. We will learn about addition and subtraction of matrices.

    • 27:08
  • In this module we will learn about Num-Pie Linear Algebra to work on Python. It further includes the understanding of the use of functions - #dot, #vdot, #inner, #matmul, #determinant, #solve, #inv. Basic examples of the #dot, #vdot functions will be discussed.

    • 13:55
  • In this module we will learn about how the #inner function work in a two-dimension array. We will also learn its usage in #dot and #vdot. We will see explanation of the functions solving examples.

    • 13:39
  • In this module we will learn about using #matmul function. We will learn about normal product and stack of arrays. We will also learn how to check the dimensions of the array and how to make it compatible.

    • 11:20
  • In this module we will learn about the #determinant function. The basics of the #determinant function will be explained. Examples will be solved with explanations to understand it.

    • 06:46
  • In this module we will learn what a Determinant is. We will also learn about how to find a Determinant. We will further learn how to find the Determinant of a 2*2 and 3*3 matrix learn about the basics of #inv function.

    • 08:40
  • In this module we will learn about the #inv function. We will learn about how to find the inverse of a matrix. We will also learn how to find the Identity matrix for the inverse.

    • 16:32
  • In this module we will discuss about the inverse of a matrix. We will understand what an Inverse is. We will further learn how the Inverse of a matrix is found.

    • 16:44
  • In this module we will learn about the difference of the dot( ) and the inner( ). We will see examples of dot( ) and inner( ), We will also learn about the dissimilarities between the dot( ) and inner( ) with the help of examples.

    • 27:37
  • In this module we will learn about numpy matrix. We will learn the different ways of creating a matrix. We will also learn about a vector as a matrix and its multiplication with matrix.

    • 16:27
  • In this module we will learn about the #numpy.vdot( ) function. This module is a continuation of the previous module. We will also learn about the #numpy,inner( ) function.

    • 16:24
  • In this module we will understand the different concepts like Rank, Determinant, Trace, etc, of an array. Then we will learn how to find the item value of a matrix. We will also learn about the matrix and vector products.

    • 25:27
  • In this module we will learn about the matrix and vector products. We will learn about how it works on imaginary and complex numbers. We will also get an understanding of matmul( ), inner( ), outer( ), linalg.multi_dot( ), tensordot( ), einsom( ), einsum_path( ),linalg.matrix_power( ).

    • 25:02
  • In this module we will learn about the basics of #inverse of a matrix. We will understand what an Inverse is. We will also see examples of inverse of a matrix and learn how to calculate it.

    • 16:44
  • In this module we will learn about the basics of Python. We will also learn about the Packages needed by the machine language. We will further learn the basics of numpy, scipy, pandas, skikit-learn, etc. needed machine learning and data science.

    • 16:18
  • In this module we will understand about SciPy. We will also learn about SkiKit-learn and Theano. We will further learn about TensorFlow, Keras, PyTorch, Pandas, Matplotlib.

    • 19:19
  • In this module we will see examples of the topics discussed in the previous module. We will also start the basics of Python. We will also solve some basic problems.

    • 18:50
  • In this module we will continue the basic problems of Python. We will also understand about Operators. We will also see the different operators and its applications.

    • 21:36
  • In this module we will continue learning the different Operators. We will also learn about Advanced Data types. We will learn and understand the different data types and about Sets.

    • 18:39
  • In this module we will learn about list. We will see the different functions of list. We will also learn about Jupyter notebook.

    • 17:46
  • In this module we will learn about #condition statements in Python in brief. We will also learn about the applications of #condition statements We will solve some examples to understand the #condition statements.

    • 25:44
  • In this module we will learn about the Loop in Pyhton. We will also learn about the different kinds of loops. We will see examples of For loop, and break keyword.

    • 16:48
  • In this module we will continue with the #for loop. We will also learn about the continue keyword. We will solve examples for the usage of the keywords.

    • 20:48
  • In this module we will learn about Functions in Python. We will solve examples using different functions. We will understand how functions work.

    • 16:53
  • In this module we will learn about arguments in functions. We will also solve examples to understand the usage of arguments in functions. We will also learn about #call by reference in Python.

    • 18:40
  • In this module we will learn about strings. We will also learn about types of arguments for functions in python. We will also see the usage of the different types of arguments.

    • 10:36
  • In this module we will learn about default arguments. We will also learn about variable arguments. We will solve examples to understand it better.

    • 06:37
  • In this module we will learn about the remaining arguments. We will understand about default and variable arguments better. We will also learn about keyword arguments.

    • 32:18
  • In this module we will learn about built-in functions. We will also learn about the different built-in functions in python. We will solve examples to understand the functions better.

    • 37:15
  • In this module we will continue the previous functions. We will also learn about other built-in functions. We will also learn about bubble sort in python.

    • 09:44
  • In this module we will learn about the scope of variable in function. We will also learn about the different variables and its usage. We will solve examples using the different variables to understand it better.

    • 13:15
  • In this module we will learn about the math module in python. We will learn about the different inbuilt functions that deal with math functions. We will solve problems using the different math functions.

    • 12:52
  • In this module we will continue with the previous lecture. We will also learn about the different arguments in functions. We will also learn about call by reference in python.

    • 18:40
  • In this module we will continue with the previous lecture. We will also start mathplotlib in python. We will learn the different types of mathplotlib by using jupyter.

    • 1:04:09
  • In this module we will learn about loan calculator using tkinter. We will also learn how to use the loan calculator. We will solve an example to understand its usage.

    • 33:36
  • In this module we will continue with the previous lecture. We will learn how to compute payments using functions. We will also learn about the function getmonthlypayment.

    • 36:44
  • In this module we will learn about numpy function. We will also learn about mathematical and logical operations using numpy. We will also be explained about different numpy arrays.

    • 17:05
  • In this module we will continue with the previous lecture. We will learn about different numpy attributes. We will solve examples using the different attributes and slicing an array.

    • 24:43
  • In this module we will learn about advanced slicing of an array. We will use jupyter to do array slicing. We will understand detail how array slicing works.

    • 29:55
  • In this module we will learn about using jupyter notebook online. We will also learn about ranges. We will learn about creating arrays from ranges. We will also learn about linear space.

    • 28:43
  • In this module we will learn about the average function. We will also learn about the different averages. We will solve examples to understand the function.

    • 21:25
  • In this module we will learn about generating random strings and passwords. We will also learn about generating a string of lower and upper case letters. We will solve examples using the different strings.

    • 21:25
  • In this module we will learn about generating strings. We will also learn about upper case letters and only printing specific letter. We will also learn about alpha numeric letters.

    • 22:46
  • In this module we will learn about the unique function. We will continue using arrays. We will solve example using unique functions in arrays.

    • 16:52
  • In this module we will learn about array manipulation function, We will learn about the delete function in numpy. We will solve examples for better understanding.

    • 10:24
  • In this module we will learn about the insert function in numpy. We will also learn about flattened array. We will solve examples.

    • 10:22
  • In this module we will learn about examples with two dimension arrays. We will also learn about the ravel function. We will also learn about the rollaxis function, swapaxes function.

    • 14:43
  • In this module we will learn about statistical functions. We will also learn about min and max values. We will solve examples using the functions.

    • 06:22
  • In this module we will learn about functions for rounding. We will also learn about round off function, floor function and ceil function. We will solve examples using the functions.

    • 14:16
  • In this module we will learn about numpy append function. We will also learn about resize function. We will solve examples.

    • 25:20
  • In this module we will learn about numpy nonzero function. We will also learn about the where function. We will solve examples using the different functions.

    • 14:23
  • In this module we will learn about matrix library. We will also learn about the different matlib functions We will solve different examples using the matlib function. vvvv

    • 18:25
  • In this module we will learn about the basic operations that can be done on numpy arrays. We will also learn about arithmetic operations and functions. We will do examples with arithmetic operations.

    • 14:40
  • In this module we will learn about numpy filter array. We will do programs on numpy filter array. We will solve examples using the filter array.

    • 16:35
  • In this module we will learn about array manipulation functions. We will see how the array manipulation functions work. We will learn about the different manipulation functions.

    • 29:18
  • In this module we will learn about broadcasting function in numpy. We will also learn about reshape in numpy. We will also learn about removing function in numpy.

    • 24:23
  • In this module we will learn about indexing. We will also learn about slicing. We will solve examples to understand the concept.

    • 15:39
  • In this module we will learn about numpy append function. We will also learn about resize function. We will solve examples using the functions.

    • 25:20
  • In this module we will learn about conversion of numpy dtypes to native python types. We will also learn to create 4*4 matrix in which 0 and 1 are staggered with zero on the main diagonal. We will also learn to create 10*10 matrix elements on the borders will be equal to 1 and inside 0.

    • 22:11
  • In this module we will learn how to use a python program to find the maximum and minimum value of a flattened array. We will also see the function called flat and flatten to make the array flattened. We will learn about function import numpy as np and array-np.arrange( )

    • 19:44
  • In this module we will learn how to generate a random string of a given length. Tutor will address the issues faced in generating random strings. Further in the video, we will discuss the various ways in which generation of a random staring can be performed.

    • 21:46
  • In this video we will be covering on creating a simple project. We will see the practical on how tutor creates a simple project. We will also see some examples on how to create a simple project. The video talks about how to get common items between 2 python numpy arrays.

    • 21:23
  • In this video we will talk about another function in python programming called the split function. The function split divides the arrays into sub arrays. The split() method splits a string into an array of substrings. The split() method returns the new array. The split() method does not change the original string. If (" ") is used as separator, the string is split between words.

    • 11:53
  • This video is a sequel of explanation of spilt function. We will discuss the three types of split functions – 1. Normal split, 2. Horizontal split and 3. Vertical Split. Further we will discuss the roles of split function and what do they do.

    • 12:18
  • In this video we will learn about the numpy filter array. We will further see what is filtering of array. Getting some elements out of an existing array and creating a new array out of them is called filtering of array, using a bullion index list.

    • 16:35
  • In this video we will learn about an important topic in Python, i.e Python file handling. We will see what is a file and the type of executable files. Further we will see what is output and how to view the output. Different access modes that can be opened with the file.

    • 14:08
  • In this video we will see an example on how to open and file in view mode, by giving the name of the file. File statement in Python is used in exception handling to make the code cleaner and much more readable. It simplifies the management of common resources like file streams. ... with open ( 'file_path' , 'w' ) as file : file .

    • 23:38
  • This video is a continuation of file system tutorial. Here we will see to use the append mode and what is append mode. Python has a built-in open() function to open a file. This function returns a file object, also called a handle, as it is used to read or modify the file accordingly. We can specify the mode while opening a file. In mode, we specify whether we want to read r , write w or append a to the file.

    • 28:56
  • In this module we will start a new topic known as random module which is a very important part in numpy. Further we will discuss the functionalities of random module to generate random numbers.

    • 14:48
  • In this module we will see how to generate the arrays on float and hot generate a single floating value from 0 to 1. Further we will see taking array as a parameter and randomly return one of the values.

    • 19:02
  • In this module we will learn the random module in continuations. The random is the module present in the numpy library. This module contains simple random generation methods.

    • 22:41
  • In this module how random module contains functions used to generate random numbers. We will also see some permutations and distribution functions.

    • 22:41
  • In this module we will see the choice functions and the different variants of choice function. Further we will see how to randomly select multiple choices from the list. Random.sample or random.choices are the functions used to select multiple choices or set.

    • 10:03
  • In this module we will see the difference between the sample function and the choices functions. Further, we will do a random choice from asset with Python, by converting it to tuple.

    • 09:21
  • In this module we will learn about the random Boolean in Python, using random.choice. In order to generate Random boolean, we use the nextBoolean() method of the java. util. Random class. This returns the next random boolean value from the random generator sequence

    • 43:00
  • In this module we will learn about the library available in python that is called Pandas. We will see how Pandas is one of the important tools available in Python. Further we will see how Pandas makes sense to list the things.

    • 15:57
  • In this module we will learn about the basics of Pandas. Further we will see how this an important tool for Data scientist and Analysts and how pandas is the back bone of most of the data projects.

    • 05:53
  • This module is a sequel of the previous tutorial on Pandas. In this module we will see practical project on pandas using series and dataframes. Lastly we will learn how to handle duplicate and how to handle information method and shape attribute.

    • 32:00
  • In this video we will see about column clean and how to clean the column. Further we will see how to rename the columns by eliminating symbols and other different ways.

    • 20:58
  • In this module we will learn about how to work with the missing values or null values. Further we will see if the dataset is inconsistent or has some missing values then how to deal with the missing values when exploring the data.

    • 28:10
  • In this video we will see how to perform the imputation on column, i.e., metascore which has some null values. Further we will see how to use describe function on the genre column of the dataset.

    • 15:19
  • In this module we will learn about the frequency of columns. Further we will see about the functio0n called value counts. The value counts function when used on the genre column tells us the frequency of all the columns.

    • 08:09
  • In this video we will learn about the methods of slicing, selecting and extracting. If these methods are not followed properly then we will receive attribute errors. Further we will learn to manipulate and extract data using column headings and index locations.

    • 18:57
  • 2.7 MATPLOTLIB BASICS

    • 20:22
  • 2.7.1 MATPLOTLIB BASICS

    • 20:38
  • 2.7.2 MATPLOTLIB BASICS

    • 17:32
  • 2.7.3 MATPLOTLIB BASICS

    • 04:00
  • 2.7.4 MATPLOTLIB BASICS

    • 11:31
  • 2.7.5 MATPLOTLIB BASICS

    • 07:23
  • 2.7.6 MATPLOTLIB BASICS

    • 16:55
  • 2.7.7 MATPLOTLIB BASICS

    • 11:53
  • 2.7.8 MATPLOTLIB BASICS

    • 17:16
  • 2.7.9 MATPLOTLIB BASICS

    • 17:40
  • 2.7.9.1 MATPLOTLIB BASICS

    • 16:55
  • 2.7.9.11 MATPLOTLIB BASICS

    • 20:38
  • 2.8 AGE CALCULATOR APP

    • 26:46
  • 2.8.1 AGE CALCULATOR APP

    • 12:22
  • 2.8.2 AGE CALCULATOR APP

    • 33:00
  • 2.8.3 AGE CALCULATOR APP

    • 37:56
  • 3.1 MACHINE LEARNING BASICS

    • 27:27
  • 3.1.1 MACHINE LEARNING BASICS

    • 17:31
  • 3.1.2 MACHINE LEARNING BASICS

    • 17:36
  • 3.1.3 MACHINE LEARNING BASICS

    • 15:38
  • 3.1.4 MACHINE LEARNING BASICS

    • 13:53
  • 3.1.5 MACHINE LEARNING BASICS

    • 11:55
  • 3.1.6 MACHINE LEARNING BASICS

    • 18:51
  • 3.1.7 MACHINE LEARNING BASICS

    • 23:55
  • 3.1.8 MACHINE LEARNING BASICS

    • 22:38
  • 3.1.9 MACHINE LEARNING BASICS

    • 29:13
  • 3.1.9.1 MACHINE LEARNING BASICS

    • 17:36
  • 3.2 MACHINE LEARNING BASICS

    • 08:08
  • 4.1 TYPES OF MACHINE LEARNING

    • 35:11
  • 4.1.1 TYPES OF MACHINE LEARNING

    • 15:03
  • 4.1.2 TYPES OF MACHINE LEARNING

    • 16:18
  • 4.1.3 TYPES OF MACHINE LEARNING

    • 13:58
  • 4.1.4 TYPES OF MACHINE LEARNING

    • 19:45
  • 4.1.5 TYPES OF MACHINE LEARNING

    • 05:12
  • 4.1.6 TYPES OF MACHINE LEARNING

    • 31:39
  • 5.1 TYPES OF MACHINE LEARNING

    • 28:19
  • 5.1.1 TYPES OF MACHINE LEARNING

    • 31:56
  • 5.1.2 TYPES OF MACHINE LEARNING

    • 25:08
  • 5.1.3 TYPES OF MACHINE LEARNING

    • 46:37
  • 5.1.4 TYPES OF MACHINE LEARNING

    • 31:00
  • 5.1.5 TYPES OF MACHINE LEARNING

    • 24:21
  • 5.1.6 TYPES OF MACHINE LEARNING

    • 16:20
  • 5.1.7 TYPES OF MACHINE LEARNING

    • 32:53
  • 5.1.8 TYPES OF MACHINE LEARNING

    • 56:20
  • 5.2 MULTIPLE REGRESSION

    • 34:30
  • 5.2.1 MULTIPLE REGRESSION

    • 37:35
  • 5.2.2 MULTIPLE REGRESSION

    • 40:56
  • 5.2.3 MULTIPLE REGRESSION

    • 56:04
  • 5.2.4 MULTIPLE REGRESSION

    • 46:41
  • 5.2.5 MULTIPLE REGRESSION

    • 38:14
  • 5.2.6 MULTIPLE REGRESSION

    • 37:48
  • 5.2.7 MULTIPLE REGRESSION

    • 1:01:26
  • 5.3 KNN INTRO

    • 26:49
  • 5.3.1 KNN ALGORITHM

    • 48:57
  • 5.3.2 KNN ALGORITHM

    • 11:17
  • 5.3.3 INTRODUCTION TO CONFUSION MATRIX

    • 42:22
  • 5.3.4 INTRODUCTION TO SPLITTING THE DATASET USING TRAINTESTSPLIT

    • 24:37
  • 5.3.5 KNN ALGORITHM

    • 50:29
  • 5.3.6 KNN ALGORITHM

    • 56:10
  • 5.4 INTRODUCTION TO DECISION TREE

    • 44:37
  • 5.4.1 INTRODUCTION TO DECISION TREE

    • 39:32
  • 5.4.2 DECISION TREE ALGORITHM

    • 36:41
  • 5.4.3 DECISION TREE ALGORITHM

    • 20:10
  • 5.4.4 DECISION TREE ALGORITHM

    • 55:37
  • 5.5 UNSUPERVISED LEARNING

    • 23:26
  • 5.5.1 UNSUPERVISED LEARNING

    • 09:16
  • 5.5.2 UNSUPERVISED LEARNING

    • 18:28
  • 5.5.3 UNSUPERVISED LEARNING

    • 29:50
  • 5.5.4 AHC ALGORITHM

    • 46:30
  • 5.5.5 AHC ALGORITHM

    • 19:55
  • 5.6 KMEANS CLUSTERING

    • 23:08
  • 5.6.1 KMEANS CLUSTERING

    • 30:25
  • 5.6.2 KMEANS CLUSTERING

    • 1:01:04
  • 5.6.3 DBSCAN ALGORITHM

    • 37:09
  • 5.6.4 DBSCAN PROGRAM

    • 32:45
  • 5.6.5 DBSCAN PROGRAM

    • 49:56

Course/Topic 7 - Machine Learning with Python - all lectures

  • In this lecture session we learn about basic introduction to machine learning and also talk about This machine learning process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms.

    • 40:25
  • In this lecture session we learn about types of machine learning in machine learning and also talk about their primary three types of machine learning we also explore and understand the different types of machine learning.

    • 43:26
  • In this lecture session we learn about Supervised, Unsupervised, and Reinforcement Learning in brief and also talk about some features and factors of Supervised, Unsupervised, and Reinforcement machine Learning.

    • 49:33
  • In this lecture session we learn about The primary rationale for adopting Python for machine learning is because it is a general purpose programming language that you can use both for research and development and in production. In this post you will discover the Python ecosystem for machine learning.

    • 28:23
  • In this tutorial we learn about components of python ML Ecosystem in machine learning and also talk about features and factors of Object-Oriented Language: One of the key features of python is Object-Oriented programming. Python supports object-oriented language and concepts of classes, object encapsulation, etc.

    • 38:59
  • In this tutorial we learn about what pandas is in machine learning and also talk about the pandas package of the most important tool in machine learning and all different tools in brief.

    • 1:01:47
  • In this lecture session we learn about The most common data format for ML projects is CSV and it comes in various flavors and varying difficulties to parse. In this section, we are going to discuss three common approaches in Python to load CSV data files .

    • 13:13
  • In this tutorial we learn about regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed and also talk about different types of Regression analysis.

    • 38:11
  • In this tutorial we learn about how Linear regression is used to predict the value of a continuous dependent variable with the help of independent variables. Logistic and also talk about linear regression is both a statistical and a machine learning algorithm. Linear regression is a popular and uncomplicated algorithm used in data science and machine learning.

    • 1:08:44
  • In this lecture session we learn about the scikit-learn library in machine learning and also talk about what Scikit-Learn is, how it’s used, and what its basic terminology is. While Scikit-learn is just one of several machine learning libraries available in Python, it is one of the best known. The library provides many efficient versions of a diverse number of machine learning algorithms.

    • 33:20
  • In this lecture session we learn about creating a train and test dataset in machine learning and also talk about The test data set contains data you are going to apply your model to. In contrast, this data doesn’t have any "expected" output. During the test phase of machine learning, this data is used to estimate how well your model has been trained and to estimate model properties.

    • 48:09
  • In this tutorial we learn about multiple regression is the extension of ordinary least-squares (OLS) regression because it involves more than one explanatory variable. Multiple linear regression (MLR), also known simply as multiple regression, is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.

    • 29:27
  • In this lecture session we learn about examples of multiple linear regression in machine learning and also talk about features and functions of Linear regression that can only be used when one has two continuous variables—an independent variable and a dependent variable.

    • 56:30
  • In this tutorial we learn about Polynomial Regression is a regression algorithm that models the relationship between a dependent (y) and independent variable (x) as nth degree polynomial. The Polynomial Regression equation is given below: It is also called the special case of Multiple Linear Regression in ML.

    • 1:01:15
  • In this lecture session we learn about classification in machine learning as a supervised learning approach and also talk about attempts to learn between a set of variables and a target set of variables of a test.

    • 47:34
  • In this tutorial we learn about Logistic regression models to help you determine a probability of what type of visitors are likely to accept the offer — or not. As a result, you can make better decisions about promoting your offer or make decisions about the offer itself and also talk about The three types of logistic regression are: Binary logistic regression is the statistical technique used to predict the relationship between the dependent variable (Y) and the independent variable (X), where the dependent variable is binary in nature. For example, the output can be Success/Failure, 0/1 , True/False, or Yes/No.

    • 1:07:05
  • In this lecture session we learn about what KNN K-nearest neighbors (KNN) is a type of supervised learning algorithm used for both regression and classification. KNN tries to predict the correct class for the test data by calculating the KNN

    • 35:53
  • In this lecture session we learn about encoding data columns in machine learning Encoding is the process of converting the data or a given sequence of characters, symbols, alphabets etc., into a specified format, for the secured transmission of data. Decoding is the reverse process of encoding which is to extract the information from the converted format. Data Encoding.

    • 41:41
  • In this tutorial we learn about decision trees in machine learning. Decision tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.

    • 44:02
  • In this lecture session we learn about Support Vector Machine Algorithm. Support Vector Machine (SVM) is a supervised machine learning algorithm used for both classification and regression. Though we say regression problems as well, it's best suited for classification.

    • 35:02
  • In this lecture session we learn about An Overview of Clustering in the Cloud. Computer clusters, and in particular Kubernetes clusters, have seen a substantial rise in adoption in the last decade. Startups and tech giants alike are leveraging cluster-based architectures to deploy and manage their applications in the cloud.

    • 57:03
  • In this lecture session we learn about Cluster analysis is an essential human activity. Cluster analysis is used to form groups or clusters of the same records depending on various measures made on these records. The key design is to define the clusters in ways that can be useful for the objective of the analysis.

    • 57:32
  • In this lecture session we learn about Hierarchical clustering begins by treating every data point as a separate cluster. Then, it repeatedly executes the subsequent steps: Merge the 2 maximum comparable clusters. We need to continue these steps until all the clusters are merged together. In Hierarchical Clustering, the aim is to produce a hierarchical series of nested clusters.

    • 1:01:57
  • In this tutorial we learn about implementation of Agglomerates hierarchical clusters in machine learning and also talk about features of hierarchical clusters.

    • 45:13
  • In this tutorial we learn about Association Rule Learning is a rule-based machine learning technique that is used for finding patterns (relations, structures etc.) in datasets. By learning these patterns we will be able to offer some items to our customers.

    • 1:11:02
  • In this tutorial we learn about Data Mining enables users to analyze, classify and discover correlations among data. One of the crucial tasks of this process is Association Rule Learning. An important part of data mining is anomaly detection, which is a procedure of search for items or events that do not correspond to a familiar pattern.

    • 1:16:49
  • In this lecture session we learn that Recommender systems are so commonplace now that many of us use them without even knowing it. Because we can't possibly look through all the products or content on a website, a recommendation system plays an important role in helping us have a better user experience, while also exposing us to more inventory we might not discover otherwise.

    • 32:55
  • In this lecture session we learn about Recommender Function. An important component of any of these systems is the recommender function, which takes information about the user and predicts the rating that user might assign to a product, for example. Predicting user ratings, even before the user has actually provided one, makes recommender systems a powerful tool.

    • 1:00:18
  • In this lecture session we learn about Collaborative filtering is a difference of memory-based reasoning especially well appropriated to the application of supporting personalized recommendations. A collaborative filtering system begins with a history of personal preferences.

    • 20:44
  • In this tutorial we learn about implementation of move recommender systems in machine learning and also talk about features and functions of implementation of move recommender systems in brief.

    • 1:17:16

Course/Topic 8 - Deep Learning Foundation - all lectures

  • In this session we will learn about the introduction to Deep Learning. This video talks about Deep Learning as a series introduction and what is a neural network. Furthermore, we will talk about the 3 reasons to go deep and your choice of Deep net.

    • 52:39
  • In this video tutorial we will discuss about the neural networks and the 3 reasons to go Deep. Further we will also learn about the use of GPU in artificial intelligence and your choice of deep net.

    • 30:06
  • In this session we will learn about the deep learning models basics. After this video you will be able to understand the concept of restricted Boltzmann machines and deep belief network. Furthermore, you will learn about the convolution neural network and recurrent neural network.

    • 43:00
  • In this video course further topics of Deep learning models. After this video you will be able to understand the convolution neural network and its characteristics in detail.

    • 1:26:51
  • In this video course further topics of Deep learning models. After this video you will be able to understand the recurrent neural network and its characteristics.

    • 15:43
  • In this session the tutor talks about the basic Additional Deep Learning Models. In this video you will learn about Auto encoders, Recursive neural tensor network and generative adversarial networks

    • 44:28
  • This session is in continuation to the previous session. In this video we will learn about the Recursive Neural Tensor Network in detail and hierarchical structure of data.

    • 31:44
  • In this Additional Deep Learning Models tutorial, we will proceed with the Generative Adversarial Networks (GAN) and its uses.

    • 22:22
  • In this video the tutor explains the Platforms and Libraries of Deep Learning. We will start with what is a deep net platform, H2O.ai and Dato Graph Lab. Further we will see what is a Deep Learning Library and Theano and Caffe. We will also cover a bit of Keras and TensorFlow.

    • 53:43
  • This tutorial will cover the further part of DatoGraph Lab and its history. Further we wil see the benefits and uses of DatoGraph Lab.

    • 28:19
  • This tutorial will cover the further part of DatoGraph Lab and its history.

    • 28:15
  • In this video we will cover the further topics of Deep Learning platform and Libraries such as what is a Deep Learning Library? when and how to use Theano and Caffe as Deep Learning Library.

    • 29:19
  • In previous video we have leant about Theano and Caffe Deep Learning Library. In this video we will learn about the TensorFlow (free and open source library) as a Deep Learning Library and building Deep Learning Models.

    • 40:18
  • In this video we will learn about the last type of Library i.e. Keras. Keras is an open source neural network library and runs on top of Theano or TensorFlow. We will further see the advantages of Keras in Deep Learning.

    • 25:34

Course/Topic 9 - Deep Learning with Keras - all lectures

  • In this first session we will be learning about introductory topics of Deep Learning. We will see about what is Deep learning and what is artificial neural network. Furthermore, in the introduction to deep learning with Keras we will see the overview, features and benefits of Keras. Lastly we will learn about the Keras installation.

    • 37:38
  • In this video tutorial we will learn about Keras as a neural based library. In this you will see the stepwise installation of keras, starting by creating a virtual environment and then activating the virtual environment.

    • 53:30
  • This video will cover the topics under Keras such as Models, Layers and Modules. In Keras Models we will learn about sequential model and functional API. In Keras layers we will learn about Dense Layer, Dropout Layer, Convolution Layer and Pooling Layer.

    • 34:38
  • This video session is a sequel to Keras – Models, Layers and Modules. In this we will learn in depth about the sequential model and the different layers in Keras.

    • 39:52
  • In this session the tutor explains about the layers in sequential model and how the model looks like and its functions. We will see how to build a sequential model successfully.

    • 23:49
  • This video talks about some methods on how to access the models. Further we will see about model.layers, model.input and model.output and understanding summarizing the model.

    • 38:26
  • This session will explain the Keras layers in detail. The tutor talks about the basic concepts of each layer - Input layer, output layer and hidden layer. Further explanation is on Dense layer and its operation. Secondly we will learn about Dropout layers and lastly about Convolution layer.

    • 26:45
  • In this video we cover the last part of this topic that is Modules. We will learn about the Modules provided by Keras such as Backend Module and Initializers Module.

    • 36:56
  • This video course will explain about the Model Compilation, evaluation and Predictions in Keras. After watching this chapter, you will be able to understand the concepts of Loss, Optimizer, Metrics and Compiling the Module. We will also learn about Model Training, Model evaluation and Model Prediction.

    • 28:26
  • This video is a sequel to the above video. In this video we will learn about the Optimizer concept in depth. In this session the tutor talks about what is Optimizer and Stochastic gradient descent (SGD) and Adam Optimizer.

    • 36:58
  • This video is a sequel to the above video. In this video we will learn about the Loss concept in depth. Further we will see how the compilation of a model, its training and evaluation.

    • 25:07
  • This video talks about the Model Training and its functions and batch size. Here we will see how to create data and code for single input model. Lastly we see about Model Prediction and why are predictions important.

    • 59:02
  • This is a video tutorial on Life-Cycle for Neural Network Models in Keras. We will look at the 5 steps in the life cycle of neural network i.e. Define Network, compile Network, Fit Network, Evaluate Network and making Predictions.

    • 35:31
  • This video is a sequel to the above video on 5 steps of Life cycle of Neural Network. In this we continue with the step 3 i.e. Fit Network and so on.

    • 27:16
  • This tutorial explains in detail how to build our first Neural Network with Keras. Further we will learn about developing and evaluating deep learning models.

    • 32:35
  • In this session tutor defines the Kera Model using sequential model. In the end we will see evaluating Keras Models.

    • 39:46
  • In this session you will discover how to create your first deep learning neural network model in Python using Keras.

    • 44:30
  • In this tutorial we will learn about building Image Classification Model with Keras. We will start with understanding what is image recognition (classification). Further we will learn about convolution neural network (CNN) and its layers. And Lastly we will see the stepwise process for building image classification model.

    • 35:17
  • In this session we will learn Image classification model with examples to clear out the concepts in detail. The tutor talks about step 2 i.e. importing libraries and splitting dataset. Step 3 – Building the CNN. Step 4 – full connection. Step 5 – Compile the model.

    • 40:57
  • This video is a sequel to the building image classification model tutorial. This video shows the step 6 i.e. Data Augmentation. Step 7 – setting train and test directories. Step 8 – Training our network.

    • 42:50
  • This is the last part of building image classification model tutorial. In this session we will learn the concluding steps i.e. Make prediction (how to load an image with keras).

    • 37:46

Course/Topic 10 - Deep Learning with TensorFlow - all lectures

  • In this lecture session we learn about the introduction of tensorflow and also talk about tensorflow is the learning framework of machine learning and deep learning.

    • 19:19
  • In this tutorial we learn that TensorFlow has significant use in voice recognition systems like Telecom, Mobile companies, security systems, search engines, etc. It uses the voice recognition systems for giving commands, performing operations and giving inputs without using keyboards, mouse.

    • 15:19
  • In this lecture session we learn about Tensorflow basic functions and also talk about Tensorflow is basically a software library for numerical computation using data flow.

    • 17:13
  • In this lecture session we learn about TensorFlow is a machine learning framework developed by Google Brain Team. It is derived from its core framework: Tensor. In TensorFlow, all the computations involve tensors. A tensor is a vector or a matrix of n-dimensions which represents the types of data.

    • 14:26
  • In this tutorial we learn about The TensorBoard enables us to monitor graphically and visually what TensorFlow is doing. Tensorflow’s name is directly derived from its core framework: Tensor. In Tensorflow, all the computations involve tensors. A tensor is a vector or matrix of n-dimensions that represents all types of data.

    • 19:32
  • In this lecture session we learn about In TensorFlow, all operations are conducted inside a graph. The group is a set of calculations that takes place successively. Each transaction is called an op node. TensorFlow makes use of a graph framework.

    • 17:32
  • In this lecture session we learn about ensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. When you’re ready to move your models from research to production, use TFX to create and manage a production pipeline.

    • 10:10
  • In this lecture session we learn about In TensorFlow, codes are written to create a graph, run a session, and execute the graph. Every variable we assign becomes a node where we can perform mathematical operations such as multiplication and addition.

    • 19:40
  • In this lecture session we learn about Tensor algebra. In mathematics, the tensor algebra of a vector space V, denoted T(V) or T•(V), is the algebra of tensors on V (of any rank) with multiplication being the tensor product.

    • 33:06
  • In this lecture session we learn about Linear algebra is the study of linear combinations. It is the study of vector spaces, lines and planes, and some mappings that are required to perform the linear transformations. It includes vectors, matrices and linear functions.

    • 27:08
  • In this lecture session we learn about Python. It is a very simple programming language so even if you are new to programming, you can learn python without facing any issues. Interesting fact: Python is named after the comedy television show Monty Python’s Flying Circus.

    • 13:55
  • In this tutorial we learn about basic introduction to python programming and also talk about features and functions of python programming in brief.

    • 13:39
  • In this lecture session we learn about Python is a popular programming language. It was created by Guido van Rossum, and released in 1991. It is used for: web development (server-side), software development, mathematics.

    • 11:20
  • In this tutorial we learn about Functions that can be both built-in or user-defined. It helps the program to be concise, non-repetitive, and organized. We can create a Python function using the def keyword. After creating a function we can call it by using the name of the function followed by parenthesis containing parameters of that particular function.

    • 06:46
  • In this tutorial we learn about There are the following advantages of Python functions. Using functions, we can avoid rewriting the same logic/code again and again in a program. We can call Python functions multiple times in a program and anywhere in a program. We can track a large Python program easily when it is divided into multiple functions.

    • 08:40
  • In this tutorial we learn about The purpose is to make your code more manageable and extensible. In-built functions in Python are the in-built codes for direct use. For example, print () function prints the given object to the standard output device (screen) or to the text stream file. In Python 3.6 (latest version), there are 68 built-in functions.

    • 16:32
  • In this lecture session we learn that These operators are used to perform similar operations as that of logical gates; there are 3 types of logical operators in python. Python Operators are the backbone of any operations and functions in the programming context. This has been a guide to Python Operators.

    • 22:12
  • In this lecture session we learn about There are various compound operators in Python like a += 5 that adds to the variable and later assigns the same. It is equivalent to a = a + 5. Python language offers some special types of operators like the identity operator or the membership operator. They are described below with examples. is and is not are the identity operators in Python.

    • 18:39
  • In this lecture session we learn about Functions that can be both built-in or user-defined. It helps the program to be concise, non-repetitive, and organized. We can create a Python function using the def keyword. After creating a function we can call it by using the name of the function followed by parenthesis containing parameters of that particular function.

    • 16:53
  • In this lecture session we learn about When the function is called, we pass along a first name, which is used inside the function to print the full name: Arguments are often shortened to args in Python documentations.

    • 18:40
  • In this tutorial we learn about There are 3 types of arguments that you'll most likely encounter while writing an argumentative essay. These are: 1. Classical Argument The classical or Aristotelian model of argument is the most common type of argument. It was developed by a Greek philosopher and rhetorician, Aristotle.

    • 10:36
  • In this lecture session we learn about Variable type in Python: Data types are the classification or categorization of data items. It represents the kind of value that tells what operations can be performed on a particular data. Since everything is an object in Python programming, data types are actually classes and variables are instances (object) of these classes.

    • 06:37
  • In this lecture session we learn about functions by calling the function with required arguments, without having to worry about how they actually work. There's a whole wealth of built-in functions in Python. In this post, we shall see how we can define and use our own functions.

    • 32:18
  • In this lecture session we learn about how to find the number is prime or not and also talk about more examples of finding prime numbers

    • 20:48
  • In this lecture session we learn about Num function in python programming and also talk about different functions in python in brief.

    • 17:05
  • In this tutorial we learn about Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

    • 1:04:09
  • In this tutorial we learn about the points that explain the advantages of NumPy: 1 The core of Numpy is its arrays. ... 2 Numpy supports some specific scientific functions such as linear algebra. ... 3 Numpy supports vectorized operations, like element wise addition and multiplication, computing Kronecker product, etc.

    • 24:43
  • In this lecture session we learn about At the core of the NumPy package, is the ndarray object. This encapsulates n -dimensional arrays of homogeneous data types, with many operations being performed in compiled code for performance. There are several important differences between NumPy arrays and the standard Python sequences.

    • 29:55
  • In this lecture session we learn about The Python library to do the mathematical operations in a flexible manner is called Pandas library. This is an open-source library used in data analysis and also in data manipulation so that data scientists can retrieve information from the data. It has a BSD license, and the number tables are manipulated easily.

    • 15:57
  • In this lecture session we learn about Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure.

    • 05:53
  • In this tutorial we learn about Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called indexes. A pandas Series can be created using the following constructor.

    • 32:00
  • In this lecture session we learn about Series is a one-dimensional, labeled data structure present in the Pandas library. The axis label is collectively known as index. Series structure can store any type of data such as integer, float, string, python objects, and so on.

    • 20:58
  • In this lecture session we learn about Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called indexes.

    • 28:10
  • In this tutorial we learn about String is an array of sequenced characters and is written inside single quotes, double quotes or triple quotes. Also, Python doesn’t have character data type, so when we write ‘a’, it is taken as a string with length 1.

    • 15:19
  • In this lecture session we learn about Handling Missing Values in Python In this post, we will discuss: How to check for missing values Different methods to handle missing values Real life data sets often contain missing values. There is no single universally acceptable method to handle missing values.

    • 08:09
  • In this lecture session we learn about The slice() function that returns a slice object. A slice object is used to specify how to slice a sequence. You can specify where to start the slicing, and where to end. You can also specify the step, which allows you to e.g. slice only every other item.

    • 18:57
  • In this lecture session we learn that Python comes with functions that enable creating, opening, closing, reading, and writing files built-in. Opening a file in Python is as simple as using the open () function that is available in every Python version. The function returns a "file object.

    • 14:08
  • In this lecture session we learn about We use the open () function in Python to open a file in read or write mode. As explained above, open () will return a file object. To return a file object we use open () function along with two arguments, that accepts filename and the mode, whether to read or write. So, the syntax being: open (filename, mode).

    • 23:38
  • In this lecture session we learn about Before you can write to or read from a file, you must open the file first. To do this, you can use the open () function that comes built into Python. The function takes two arguments or parameters: one that accepts the file's name and another that saves the access mode. It returns a file object and has the following syntax.

    • 14:48
  • In this lecture session we learn about The Machine Learning process starts with inputting training data into the selected algorithm. Training data being known or unknown data to develop the final Machine Learning algorithm. The type of training data input does impact the algorithm, and that concept will be covered further momentarily.

    • 27:27
  • In this tutorial we learn Machine learning ( ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.

    • 17:31
  • In this lecture session we learn about In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression.

    • 17:36
  • In this lecture session we learn about machine language, the numeric codes for the operations that a particular computer can execute directly. The codes are strings of 0s and 1s, or binary digits(“bits”), which are frequently converted both from and to hexadecimal (base 16) for human viewing and modification.

    • 15:38
  • In this lecture session we learn that machine language is a collection of binary digits or bits that the computer reads and interprets. Machine language is the only language a computer is capable of understanding. The exact machine language for a program or action can differ by operating system.

    • 13:53
  • In this lecture session we learn about Machine language. Machine language, the numeric codes for the operations that a particular computer can execute directly. The codes are strings of 0s and 1s, or binary digits (“bits”), which are frequently converted both from and to hexadecimal (base 16) for human viewing and modification.

    • 11:55
  • In this lecture session we learn about the Machine learning life cycle is a cyclic process to build an efficient machine learning project. The main purpose of the life cycle is to find a solution to the problem or project.

    • 18:51
  • In this lecture we learn about In order to predict, you have to find a function (model) that best describes the dependency between the variables in our dataset. This is called training the model. The training dataset will be a subset of the entire dataset from the pandas data frame df that you created in part two of this series.

    • 23:55
  • In this lecture session we learn about why we need a data set in machine languages and also talk about some features and functions of a data set in machine language.

    • 22:38
  • In this lecture session we learn about Data preprocessing is required for cleaning the data and making it suitable for a machine learning model which also increases the accuracy and efficiency of a machine learning model. It involves the steps below.

    • 29:13
  • In this lecture session we learn about Machine learning (ML) is a subfield of artificial intelligence (AI) that allows computers to learn to perform tasks and improve performance over time without being explicitly programmed.

    • 08:08
  • In this lecture session we learn about 1 Supervised Learning. Supervised learning is when you provide the machine with a lot of training data to perform a specific task. 2 Unsupervised Learning. 3 Reinforcement Learning.

    • 35:11
  • In this lecture session we learn about Supervised Machine Learning is an algorithm that learns from labeled training data to help you predict outcomes for unforeseen data. In Supervised learning, you train the machine using data that is well “labeled.” It means some data is already tagged with correct answers.

    • 15:03
  • In this lecture session we learn about how we determine the type of training and also talk about the importance of types of machine learning language.

    • 16:18
  • In this lecture session we learn about regression analysis that helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed.

    • 13:58
  • In this lecture session we learn about logistic regression is the statistical technique used to predict the relationship between the dependent variable (Y) and the independent variable (X), where the dependent variable is binary in nature. For example, the output can be Success/Failure, 0/1, True/False, or Yes/No.

    • 19:45
  • In this lecture session we learn about regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.

    • 05:12
  • In this tutorial we learn about how we classify the implementation of machine learning and also talk about some more examples of implementation of machine learning.

    • 31:39
  • In this lecture session we learn about Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc.

    • 28:19
  • In this lecture session we learn about A feature that is a measurable property of the object you’re trying to analyze. In datasets, features appear as columns: The image above contains a snippet of data from a public dataset with information about passengers on the ill-fated Titanic maiden voyage.

    • 17:36
  • In this lecture session we learn about basic examples of machine learning and also talk about more examples of machine learning.

    • 25:08
  • In this session we learn about developing the model in machine learning and also talk about different models and how we develop it in machine learning.

    • 31:56
  • In this lecture session we learn about how TensorFlow Playground solves this particular problem. On the Playground, click the Play button in the upper left corner. The line between blue and orange data points begins to move slowly. Hit the reset button and click play again a few times to see how the line moves with different initial values.

    • 36:00
  • In this lecture session we learn about A Perceptron is an algorithm for supervised learning of binary classifiers. This algorithm enables neurons to learn and process elements in the training set one at a time.

    • 22:00
  • In this lecture session we learn about Multi-Layer perceptrons, define the most complicated architecture of artificial neural networks and also talk about functions of multilayer perceptrons.

    • 16:45
  • In this lecture session we learn about Single Layer Perceptrons. For understanding single layer perceptrons, it is important to understand Artificial Neural Networks (ANN). Artificial neural networks is the information processing system the mechanism of which is inspired by the functionality of biological neural circuits.

    • 12:00
  • In this lecture session we learn about Artificial Intelligence (AI) Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

    • 19:39
  • In this tutorial we learn about Artificial neural networks. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal from one artificial neuron to another.

    • 11:20
  • In this tutorial we learn that there are two types of ANN. Such as FeedForward and Feedback. a. FeedForward ANN In this network flow of information is unidirectional.here are two types of ANN. Such as FeedForward and Feedback. a. FeedForward ANN In this network flow of information is unidirectional.

    • 21:55
  • In this lecture session we learn about the structure of the ANN affected by a flow of information. Hence, neural network changes were based on input and output. Basically, we can consider ANN as nonlinear statistical data.

    • 17:10
  • In this lecture session we learn about Artificial neural networks (ANNs) that consist of a node layer, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold.

    • 14:25
  • In this tutorial we learn about In order to build a model, it is recommended to have GPU support, or you may use the Google colab notebooks as well. Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

    • 14:07
  • In this tutorial we learn that there are basically two classes- “≤50K” and “>50K. However, we can not leave our target labels in the current string format. This is because TensorFlow does not understand strings as labels.

    • 17:00
  • In this lecture session we learn about The data can be accessed at my GitHub profile in the TensorFlow repository. Here is the link to access the data. My code and Jupyter notebook can be accessed below.

    • 20:00
  • In this lecture session we learn about features of Tensorflow in deep learning and also talk about some functions and importance of Tensorflow in brief.

    • 25:00
  • In this tutorial we learn about The data can be accessed at my GitHub profile in the TensorFlow repository. Here is the link to access the data. My code and Jupyter notebook can be accessed below.

    • 30:25
  • In this lecture session we learn about Linear regression strives to show the relationship between two variables by applying a linear equation to observed data.

    • 37:10
  • In this lecture session we learn about Keras used in prominent organizations like CERN, Yelp, Square or Google, Netflix, and Uber. Theano is a deep learning library developed by the Université de Montréal in 2007. Comparing Theano vs TensorFlow, it offers fast computation and can be run on both CPU and GPU. Theano has been developed to train deep neural network algorithms.

    • 21:30
  • In this lecture session we learn about TensorFlow Object Detection Object detection is a process of discovering real-world object detail in images or videos such as cars or bikes, TVs, flowers, and humans. It allows identification, localization, and identification of multiple objects within an image, giving us a better understanding of an image.

    • 08:10
  • In this tutorial we learn about The super keyword refers to the objects of immediate parent class. Before learning super keywords you must have the knowledge of inheritance in Java so that you can understand the examples given in this guide.

    • 21:00
  • In this tutorial we learn about The Blender is an open source 3D computer graphics software. With the help of Blender, we can do 3D visualizations such as still images, 3D animations, VFX shots, video editing and a lot of more cool stuff.

    • 32:40
  • In this lecture session we learn about features of super keywords and also talk about some functions and importance of super keywords in brief.

    • 18:00
  • In this lecture session we learn about deep learning CNN models to train and test, each input image will pass it through a series of convolution layers with filters (Kernels), Pooling, fully connected layers (FC) and apply Softmax function to classify an object with probabilistic values between 0 and 1.

    • 16:00
  • In this lecture session we learn about machine learning called deep learning, they're likely talking about neural networks. Neural networks are modeled after our brains.

    • 22:10
  • In this lecture session we learn about In deep learning, a convolutional neural network ( CNN/ConvNet) is a class of deep neural networks, most commonly applied to analyze visual imagery.

    • 24:00
  • In this lecture session we learn about An RNN (Recurrent Neural Network) is a type of artificial neural network that can process sequential data, recognize patterns and predict the final output.

    • 13:34
  • In this lecture session we learn about how RNNs can gain more in-depth insight into a sequence and its context from such datasets to derive significant meaning and arrive at an accurate prediction as per the targeted problem at hand.

    • 17:03
  • In this lecture session we learn that the Time series is dependent on the previous time, which means past values include significant information that the network can learn.

    • 29:41
  • In this tutorial we learn about NNs containing recurrent layers that are designed to process sequences of inputs. You can feed in batches of sequences into RNNs and it will output a batch of forecasts after going through a dense layer.

    • 17:28
  • In these lecture sessions we learn about TensorFlow includes a visualization tool, which is called the TensorBoard. It is used for analyzing Data Flow Graph and also used to understand machine-learning models.

    • 11:00

Course/Topic 11 - Interview Questions - Machine Learning - all lectures

  • In this Machine Learning Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding Machine learning like what do you understand by machine learning, differentiate between inductive and deductive learning.

    • 19:00
  • In this Machine Learning Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding Machine learning like how do classification and regression differ, what is meant by overfitting in machine learning.

    • 20:54
  • In this Machine Learning Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding Machine learning like how machine learning differs from deep learning, how is KNN different from K-means.

    • 10:42
  • In this Machine Learning Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding Machine learning like what are the different types of algorithm methods in machine learning.

    • 19:28
  • In this Machine Learning Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding Machine learning like what are the 5 popular algorithms used in machine learning.

    • 14:50
  • In this Machine Learning Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding Machine learning like what is model selection in machine learning, what are the 3 stages of building the hypotheses or model in machine learning.

    • 23:00
  • In this Machine Learning Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding Machine learning like what are the common ways to handle missing data in a dataset, what do you understand by ILP.

    • 20:01
  • In this Machine Learning Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding Machine learning like how you explain a linked list and an array.

    • 22:08
  • In this Machine Learning Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding Machine learning like what is bagging and boosting, what are the similarities between bagging and boosting.

    • 08:41
  • In this Machine Learning Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding Machine learning like what do you understand by cluster sampling, what do you know about Bayesian networks.

    • 23:16
  • In this Machine Learning Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding Machine learning like what do you understand by the F1 score, how is a decision tree pruned.

    • 23:21

Course/Topic 12 - Interview Questions - TensorFlow - all lectures

  • In this Tensor Flow Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding Tensor Flow learning like what is Tensor Flow and what do you know about Tensors.

    • 16:40
  • In this TensorFlow Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding TensorFlow learning like methods to deal with the overfitting in TensorFlow

    • 16:15
  • In this TensorFlow Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding TensorFlow learning like what do you understand by deep speech and how does TensorFlow use Python API.

    • 17:31
  • In this TensorFlow Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding TensorFlow learning like what is image dashboard in TensorFlow.

    • 21:36
  • In this TensorFlow Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding TensorFlow learning like what are neural networks and what are the advantages of using artificial neural network.

    • 37:36
  • In this TensorFlow Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding TensorFlow learning like concept of logistic regression in TensorFlow and what is bias variant decomposition.

    • 30:14
  • In this TensorFlow Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding TensorFlow learning like what is dimensionality reduction and explaining the concept of ROC curve.

    • 16:45
  • In this TensorFlow Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding TensorFlow learning like difference between CNN and RNN and what is MNIST dataset.

    • 12:55
  • In this TensorFlow Interview Questions tutorial, you will get to know about the different questions being asked by the interviewers in an interview and their answers regarding TensorFlow learning like what is the source and why is validation performed.

    • 17:39
Course Objectives Back to Top

The main objective of this Machine Learning Engineer career path course is to introduce the basic techniques, principles and applications of artificial intelligence and machine learning. Specifically:

1).To gain an in-depth perspective of AI and its foundations

2).To provide a strong foundation of fundamental concepts in Machine learning and Artificial Intelligence

3).To provide a basic exposition to the methods of Artificial Intelligence

4).To make use of different machine learning techniques to design AI machinesfor solving real-world problems

5).Have solid programming skills and know Python, R, Go, Java, SQL

6).Understand how TensorFlow and other similar technologies work

7).Have a clear understanding about technologies related to AI, including Machine Learning, Neural Networks and Deep Learning

 

Course Syllabus Back to Top

Course Overview: This course is designed to equip participants with the knowledge and skills required to pursue a career in Artificial Intelligence (AI) and Machine Learning (ML). The syllabus covers foundational concepts, practical applications, and advanced techniques in AI and ML.

 

Part 1: Introduction to AI and ML

Week 1: Introduction to Artificial Intelligence

1).Definition and history of AI

2).Types of AI: Narrow vs. General AI

3).Key applications of AI in various industries

4).Hands-on Exercise: Exploring AI use cases

 

Week 2: Fundamentals of Machine Learning

1).Definition and importance of ML

2).Types of machine learning: Supervised, unsupervised, and reinforcement learning

3).Overview of the ML workflow

4).Group Activity: Discussing ML applications in real-world scenarios

 

Part 2: Data Preparation and Exploration

Week 3: Data Collection and Preprocessing

1).Understanding data sources: Structured vs. unstructured data

2).Data cleaning techniques: Handling missing values, outliers, and inconsistencies

3).Feature selection and extraction

4).Hands-on Exercise: Cleaning and preprocessing a dataset using Python

 

Week 4: Data Visualization and Exploration

1).Importance of data visualization in ML

2).Tools for data visualization: Matplotlib, Seaborn, Tableau

3).Exploratory data analysis (EDA) techniques

4).Hands-on Exercise: Performing EDA on a dataset

 

Part 3: Supervised Learning Techniques

Week 5: Regression Algorithms

1).Introduction to regression: Linear regression, polynomial regression

2).Evaluating regression models: Metrics (MSE, R²)

3).Hands-on Exercise: Building and evaluating a regression model using Scikit-learn

 

Week 6: Classification Algorithms

1).Overview of classification: Decision trees, random forests, support vector machines (SVM)

2).Model evaluation techniques: Confusion matrix, precision, recall, F1 score

3).Hands-on Exercise: Implementing a classification model with Scikit-learn

 

Part 4: Unsupervised Learning and Advanced Techniques

Week 7: Unsupervised Learning Algorithms

1).Introduction to clustering: K-means, hierarchical clustering, DBSCAN

2).Dimensionality reduction techniques: PCA, t-SNE

3).Hands-on Exercise: Applying clustering techniques to a dataset

 

Week 8: Neural Networks and Deep Learning

1).Basics of neural networks: Architecture and functioning

2).Introduction to deep learning frameworks (TensorFlow, Keras, PyTorch)

3).Hands-on Exercise: Building a simple neural network for image classification

 

Part 5: Advanced Topics in AI and ML

Week 9: Natural Language Processing (NLP)

1).Overview of NLP and its applications

2).Techniques: Tokenization, stemming, and lemmatization

3).Introduction to libraries: NLTK, SpaCy, Hugging Face

4).Hands-on Exercise: Building a basic NLP application (e.g., sentiment analysis)

 

Week 10: Reinforcement Learning

1).Principles of reinforcement learning

2).Key concepts: Agents, environments, rewards

3).Overview of popular algorithms (Q-learning, Deep Q-Networks)

4).Group Activity: Discussing real-world applications of reinforcement learning

 

Part 6: Deployment and Real-World Applications

Week 11: Model Deployment and Monitoring

1).Overview of model deployment strategies: Batch vs. real-time

2).Tools for deployment: Flask, FastAPI, Docker

3).Monitoring and maintaining ML models in production

4).Hands-on Exercise: Deploying a trained model as a web service

 

Week 12: Capstone Project Preparation

1).Overview of capstone project objectives and expectations

2).Defining project scope: Applying AI/ML to a real-world problem

3).Initial project planning and outlining tasks

4).Group Discussion: Feedback on project proposals

 

Part 7: Capstone Project Execution

Week 13: Capstone Project Execution

1).Implementing the project using skills learned throughout the course

2).Presenting findings and solutions to the class

3).Peer reviews and discussions on project experiences

 

Recommended Resources:

1).Textbooks:

a."Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig

b."Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron

2).Online Resources:

a.Courses on Coursera, edX, and Udacity

b.AI and ML communities and forums for knowledge sharing

3).Tools:-Python, Scikit-learn, TensorFlow, Keras, Jupyter Notebook, and data visualization libraries

Assessment:

1).Weekly quizzes and assignments

2).Mid-term project focused on supervised learning techniques

3).Final capstone project showcasing a comprehensive AI/ML solution

 

Certification Back to Top

The Machine Learning Engineer Certification ensures you know planning, production and measurement techniques needed to stand out from the competition.

The entry-level annual average AI engineer salary in India is around 8 lakhs, which is significantly higher than the average salary of any other engineering graduate. At high-level positions, the AI engineer salary can be as high as 50 lakhs.

At least a bachelor's degree in computer science, data science, engineering, physics, mathematics, statistics or another quantitative subject is generally required. Certifications in AI or data science may also help you meet educational requirements and gain technical knowledge.

The Professional Certificate Program in Machine Learning & Artificial Intelligence enables you to: Learn in-person from renowned MIT faculty and leading industry practitioners. Learn essential concepts and skills needed to develop effective AI systems. Understand the challenges posed by AI in the workplace.

Uplatz online training guarantees the participants to successfully go through the Artificial Intelligence and Machine Learning Engineercertification provided by Uplatz. Uplatz provides appropriate teaching and expertise training to equip the participants for implementing the learnt concepts in an organization.

Course Completion Certificate will be awarded by Uplatz upon successful completion of the Artificial Intelligence and Machine Learning Engineeronline course.

 Best certificate for Artificial Intelligence:

1.Machine Learning by Stanford University (Coursera) ...

2.Deep Learning Specialization by Andrew Ng (Coursera) ...

3.IBM Applied AI Professional Certificate (Coursera) ...

4.IBM AI Engineering Professional Certificate (Coursera) ...

5.MicroMasters® Program in Artificial Intelligence by Columbia University (edX)

 

Career & Jobs Back to Top

A Machine Learning Engineer draws an average salary of $110.952 per year depending on their knowledge and hands-on experience. The Artificial Intelligence and Machine Learning Engineer Admin job roles are in high demand and make a rewarding career.

According to a 2019 report by Indeed, Machine Learning Engineer is the top job in terms of salary, growth of postings, and general demand.

There are more artificial intelligence jobs than skilled professionals to fill them, and the AI world has shown no signs of slowing down, so the demand is very high.

Note that salaries are generally higher at large companies rather than small ones. Your salary will also differ based on the market you work in.

1).AI and Machine Learning.

2).Machine Learning Scientist.

3).Jr Machine Learning Engineer.

4).AI - Deep Learning/Computer Vision.

 

Interview Questions Back to Top

Q1.Explain Data Science?

Ans-Data science refers the process of collecting, storing and data analysis. Data Scientists make use of the data to convey stories to tell about business decisions.

 

Q2.Define Artificial Intelligence?

Ans-Artificial Intelligence or AI comes under computer science area that focuses on the creation of intelligence machine that works and react just like human beings.

 

Q3.List out the different areas where Artificial Intelligence can be used?

Ans-Artificial Intelligence is used in many areas such as computing, computer software, speech recognition, robotics, space, media, auronauts etc..

 

Q4.Define Prolog in AI?

Ans-Prolog is a programming language in terms of logic.

 

Q5.Name the search method which consumes less memory?

Ans-The “Deep First Search” method conumes less amount of memory.

 

Q6.What is the search methodology behind A* Algorithm?

Ans-A* algorithm is based upon Depth first search methodology which shows a better way of optimization and quick route to choose a path and above everything, all characteristics comes under A* Algorithm.

 

Q7.Define Generality in AI?

Ans-Generality is the measure of easiness through which the method is adapted to varied domains of applications.

 

Q8.Define FOPL?

Ans-FOPL stands for First Order Predicate Logic. The Predicate Logic is useful

1) Useful to express assertions about certain “World”

2) A semantic based upon set theory

 

Q9.What does the FOPL language contain?

Ans-The FOPL language comprises of:

1) A set of constant symbols

2) A set of variables

3) A set of predicate symbols

4) A set of function symbols

5) The logic connectivity

6) Universal and Existential Qualifier

 

Q10.List out the search engine algorithm which consumes less memory in online search?

Ans-RBFE and SMA algorithms use a limited amount of memory.

 

Q11. Define the process to create a model?

Ans-There is totally a three-step process inorder to create a model:

1) Train the model

2) Test the model

3) Deploy the model

 

Q12. List out the three stages of building a model in machine learning?

Ans-The three stages to build a model consists of:

1) Model Building

2) Model Testing

3) Applying the model

 

Q13. List out the type of biases that occurs during sampling?

Ans-

1) Selection bias

2) Under Coverage bias

3) Survivorship bias

 

Q14. How does ROC curve work?

Ans-The ROC curve represents a graphical representation of the contrast between true positive rates and false positive rates at varied thresholds.

 

Q15. Name the preferred programming language for text analytics?

Ans-Python is the best option because of its high-performance data analysis tool support. R is more supportive for machine learning than text analysis.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Course Quiz Back to Top
Start Quiz
Q1. What are the payment options?
A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay.

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A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php

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A3. All our video courses comes with lifetime access. Once you purchase a video course with Uplatz you have lifetime access to the course i.e. forever. You can access your course any time via our website and/or mobile app and learn at your own convenience.

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A5. We do not take exam as part of the our training programs whether it is video course or live online class. These courses are professional courses and are offered to upskill and move on in the career ladder. However if there is an associated exam to the subject you are learning with us then you need to contact the relevant examination authority for booking your exam.

Q6. Can I get study material with the course?
A6. The study material might or might not be available for this course. Please note that though we strive to provide you the best materials but we cannot guarantee the exact study material that is mentioned anywhere within the lecture videos. Please submit study material request using the form https://training.uplatz.com/study-material-request.php

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A9. Overview courses are 1-2 hours short to help you decide if you want to go for the full course on that particular subject. Uplatz overview courses are either free or minimally charged such as GBP 1 / USD 2 / EUR 2 / INR 100

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A12. Career Path programs are our comprehensive learning package of video course. These are combined in a way by keeping in mind the career you would like to aim after doing career path program. Career path programs ranges from 100 hours to 600 hours and covers wide variety of courses for you to become an expert on those technologies. Check all Career Path Programs here https://training.uplatz.com/online-it-courses.php?career_path_courses=done

Q13. What are Learning Path programs?
A13. Learning Path programs are dedicated courses designed by SAP professionals to start and enhance their career in an SAP domain. It covers from basic to advance level of all courses across each business function. These programs are available across SAP finance, SAP Logistics, SAP HR, SAP succcessfactors, SAP Technical, SAP Sales, SAP S/4HANA and many more Check all Learning path here https://training.uplatz.com/online-it-courses.php?learning_path_courses=done

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Q16. Do you provide software access with video course?
A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.

Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!

Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field. However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
a) Career Counseling: Seek guidance from career counselors who can provide personalized advice and help you tailor your job search strategy.
b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

Q19. How do I enrol in Uplatz video courses?
A19. To enroll, click on "Buy This Course," You will see this option at the top of the page.
a) Choose your payment method.
b) Stripe for any Credit or debit card from anywhere in the world.
c) PayPal for payments via PayPal account.
d) Choose PayUmoney if you are based in India.
e) Start learning: After payment, your course will be added to your profile in the student dashboard under "Video Courses".

Q20. How do I access my course after payment?
A20. Once you have made the payment on our website, you can access your course by clicking on the "My Courses" option in the main menu or by navigating to your profile, then the student dashboard, and finally selecting "Video Courses".

Q21. Can I get help from a tutor if I have doubts while learning from a video course?
A21. Tutor support is not available for our video course. If you believe you require assistance from a tutor, we recommend considering our live class option. Please contact our team for the most up-to-date availability. The pricing for live classes typically begins at USD 999 and may vary.



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