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

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  1. INTRODUCTION TO LINEAR ALGEBRA

  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
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  1.1 INTRODUCTION TO LINEAR ALGEBRA

  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
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  1.2 LINEAR ALGEBRA 1

  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
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  1.2.1 LINEAR ALGEBRA 2

  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
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  1.2.2 LINEAR ALGEBRA 3

  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
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  1.2.3 LINEAR ALGEBRA 4.1

  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
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  1.2.4 LINEAR ALGEBRA 4

  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
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  1.2.5 LINEAR ALGEBRA 5.1

  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
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  1.2.6 LINEAR ALGEBRA 5

  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
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  1.2.7 LINEAR ALGEBRA

  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
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  1.2.8 LINEAR ALGEBRA

  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
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  1.2.9 LINEAR ALGEBRA

  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
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  1.2.9.1 LINEAR ALGEBRA

  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
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  1.2.9.2 LINEAR ALGEBRA

  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
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  1.2.9.2.1 LINEAR ALGEBRA

  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
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  2.1 INTRODUCTION TO PYTHON

  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
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  2.1.1 INTRODUCTION TO PYTHON

  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
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  2.1.2 PYTHON DATATYPES

  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
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  2.1.3 PYTHON OPERATORS

  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
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  2.1.4 ADVANCED DATA TYPES

  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
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  2.1.5 SIMPLE PYTHON PROGRAM

  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
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  2.1.6 PYTHON CONDITION STATEMENTS

  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
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  2.1.7 PYTHON LOOPING STATEMENTS

  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
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  2.1.8 - BREAK AND CONTINUE KEYWORDS IN PYTHON

  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
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  2.1.9 FUNCTIONS IN PYTHON

  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
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  2.1.9.1 FUNCTION ARGUMENTS

  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
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  2.1.9.2 FUNCTION REQUIRED ARGUMENTS

  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
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  2.1.9.3 DEFAULT ARGUMENTS

  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
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  2.1.9.4 VARIABLE ARGUMENTS

  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
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  2.2 BUILT IN FUNCTIONS

  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
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  2.2.1 BUILT IN FUNCTIONS

  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
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  2.2.2 SCOPE OF VARIABLES

  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
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  2.2.3 PYTHON MATH MODULE

  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
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  2.2.3.1 PYTHON MATH MODULE

  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
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  2.2.4 PYTHON MATPLOTLIB MODULE

  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
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  2.2.5 A BASIC GUI APPLICATION

  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
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  2.2.6 A BASIC GUI APPLICATION

  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
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  2.3 NUMPY BASICS

  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
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  2.3.1 NUMPY BASICS

  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
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  2.3.2 NUMPY BASICS

  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
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  2.3.3 NUMPY BASICS

  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
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  2.3.4 NUMPY BASICS

  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
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  2.3.5 NUMPY BASICS

  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
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  2.3.6 NUMPY BASICS

  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
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  2.3.7 NUMPY BASICS

  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
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  2.3.8 NUMPY BASICS

  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
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  2.3.9 NUMPY BASICS

  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
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  2.3.9.1 NUMPY BASICS

  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
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  2.3.9.2 NUMPY BASICS

  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
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  2.3.9.3 NUMPY BASICS

  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
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  2.3.9.4 NUMPY BASICS

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

  • 25:20
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  2.3.9.5 NUMPY BASICS

  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
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  2.3.9.6 NUMPY BASICS

  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
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  2.3.9.7 NUMPY BASICS

  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
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  2.3.9.8 NUMPY BASICS

  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
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  2.3.9.9 NUMPY BASICS

  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
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  2.3.9.11 NUMPY BASICS

  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
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  2.3.9.12 NUMPY BASICS

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

  • 15:39
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  2.3.9.13 NUMPY BASICS

  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
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  2.3.9.14 NUMPY BASICS

  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
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  2.3.9.15 NUMPY BASICS

  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
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  2.3.9.16 NUMPY BASICS

  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
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  2.3.9.17 NUMPY BASICS

  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
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  2.3.9.18 NUMPY BASICS

  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
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  2.3.9.19 NUMPY BASICS

  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
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  2.3.9.21 NUMPY BASICS

  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
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  2.4 FILE SYSTEM

  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
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  2.4.1 FILE SYSTEM WITH STATEMENT

  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
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  2.4.2 FILE SYSTEM READ AND WRITE

  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
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  2.5 RANDOM MODULE BASICS

  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
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  2.5.1 RANDOM MODULE BASICS

  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
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  2.5.2 RANDOM MODULE BASICS

  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
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  2.5.3 RANDOM MODULE BASICS

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

  • 22:41
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  2.5.4 RANDOM MODULE BASICS

  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
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  2.5.5 RANDOM MODULE BASICS

  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
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  2.5.6 RANDOM MODULE BASICS

  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
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  2.6 PANDAS BASICS

  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
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  2.6.1 PANDAS BASICS

  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
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  2.6.2 PANDAS BASICS

  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
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  2.6.3 PANDAS BASICS

  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
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  2.6.4 PANDAS BASICS

  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
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  2.6.5 PANDAS BASICS

  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
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  2.6.6 PANDAS BASICS

  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
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  2.6.7 PANDAS BASICS

  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
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  2.7 MATPLOTLIB BASICS

  2.7 MATPLOTLIB BASICS

  • 20:22
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  2.7.1 MATPLOTLIB BASICS

  2.7.1 MATPLOTLIB BASICS

  • 20:38
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  2.7.2 MATPLOTLIB BASICS

  2.7.2 MATPLOTLIB BASICS

  • 17:32
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  2.7.3 MATPLOTLIB BASICS

  2.7.3 MATPLOTLIB BASICS

  • 04:00
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  2.7.4 MATPLOTLIB BASICS

  2.7.4 MATPLOTLIB BASICS

  • 11:31
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  2.7.5 MATPLOTLIB BASICS

  2.7.5 MATPLOTLIB BASICS

  • 07:23
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  2.7.6 MATPLOTLIB BASICS

  2.7.6 MATPLOTLIB BASICS

  • 16:55
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  2.7.7 MATPLOTLIB BASICS

  2.7.7 MATPLOTLIB BASICS

  • 11:53
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  2.7.8 MATPLOTLIB BASICS

  2.7.8 MATPLOTLIB BASICS

  • 17:16
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  2.7.9 MATPLOTLIB BASICS

  2.7.9 MATPLOTLIB BASICS

  • 17:40
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  2.7.9.1 MATPLOTLIB BASICS

  2.7.9.1 MATPLOTLIB BASICS

  • 16:55
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  2.7.9.11 MATPLOTLIB BASICS

  2.7.9.11 MATPLOTLIB BASICS

  • 20:38
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  2.8 AGE CALCULATOR APP

  2.8 AGE CALCULATOR APP

  • 26:46
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  2.8.1 AGE CALCULATOR APP

  2.8.1 AGE CALCULATOR APP

  • 12:22
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  2.8.2 AGE CALCULATOR APP

  2.8.2 AGE CALCULATOR APP

  • 33:00
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  2.8.3 AGE CALCULATOR APP

  2.8.3 AGE CALCULATOR APP

  • 37:56
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  3.1 MACHINE LEARNING BASICS

  3.1 MACHINE LEARNING BASICS

  • 27:27
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  3.1.1 MACHINE LEARNING BASICS

  3.1.1 MACHINE LEARNING BASICS

  • 17:31
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  3.1.2 MACHINE LEARNING BASICS

  3.1.2 MACHINE LEARNING BASICS

  • 17:36
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  3.1.3 MACHINE LEARNING BASICS

  3.1.3 MACHINE LEARNING BASICS

  • 15:38
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  3.1.4 MACHINE LEARNING BASICS

  3.1.4 MACHINE LEARNING BASICS

  • 13:53
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  3.1.5 MACHINE LEARNING BASICS

  3.1.5 MACHINE LEARNING BASICS

  • 11:55
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  3.1.6 MACHINE LEARNING BASICS

  3.1.6 MACHINE LEARNING BASICS

  • 18:51
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  3.1.7 MACHINE LEARNING BASICS

  3.1.7 MACHINE LEARNING BASICS

  • 23:55
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  3.1.8 MACHINE LEARNING BASICS

  3.1.8 MACHINE LEARNING BASICS

  • 22:38
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  3.1.9 MACHINE LEARNING BASICS

  3.1.9 MACHINE LEARNING BASICS

  • 29:13
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  3.1.9.1 MACHINE LEARNING BASICS

  3.1.9.1 MACHINE LEARNING BASICS

  • 17:36
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  3.2 MACHINE LEARNING BASICS

  3.2 MACHINE LEARNING BASICS

  • 08:08
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  4.1 TYPES OF MACHINE LEARNING

  4.1 TYPES OF MACHINE LEARNING

  • 35:11
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  4.1.1 TYPES OF MACHINE LEARNING

  4.1.1 TYPES OF MACHINE LEARNING

  • 15:03
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  4.1.2 TYPES OF MACHINE LEARNING

  4.1.2 TYPES OF MACHINE LEARNING

  • 16:18
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  4.1.3 TYPES OF MACHINE LEARNING

  4.1.3 TYPES OF MACHINE LEARNING

  • 13:58
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  4.1.4 TYPES OF MACHINE LEARNING

  4.1.4 TYPES OF MACHINE LEARNING

  • 19:45
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  4.1.5 TYPES OF MACHINE LEARNING

  4.1.5 TYPES OF MACHINE LEARNING

  • 05:12
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  4.1.6 TYPES OF MACHINE LEARNING

  4.1.6 TYPES OF MACHINE LEARNING

  • 31:39
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  5.1 TYPES OF MACHINE LEARNING

  5.1 TYPES OF MACHINE LEARNING

  • 28:19
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  5.1.1 TYPES OF MACHINE LEARNING

  5.1.1 TYPES OF MACHINE LEARNING

  • 31:56
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  5.1.2 TYPES OF MACHINE LEARNING

  5.1.2 TYPES OF MACHINE LEARNING

  • 25:08
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  5.1.3 TYPES OF MACHINE LEARNING

  5.1.3 TYPES OF MACHINE LEARNING

  • 46:37
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  5.1.4 TYPES OF MACHINE LEARNING

  5.1.4 TYPES OF MACHINE LEARNING

  • 31:00
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  5.1.5 TYPES OF MACHINE LEARNING

  5.1.5 TYPES OF MACHINE LEARNING

  • 24:21
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  5.1.6 TYPES OF MACHINE LEARNING

  5.1.6 TYPES OF MACHINE LEARNING

  • 16:20
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  5.1.7 TYPES OF MACHINE LEARNING

  5.1.7 TYPES OF MACHINE LEARNING

  • 32:53
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  5.1.8 TYPES OF MACHINE LEARNING

  5.1.8 TYPES OF MACHINE LEARNING

  • 56:20
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  5.2 MULTIPLE REGRESSION

  5.2 MULTIPLE REGRESSION

  • 34:30
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  5.2.1 MULTIPLE REGRESSION

  5.2.1 MULTIPLE REGRESSION

  • 37:35
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  5.2.2 MULTIPLE REGRESSION

  5.2.2 MULTIPLE REGRESSION

  • 40:56
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  5.2.3 MULTIPLE REGRESSION

  5.2.3 MULTIPLE REGRESSION

  • 56:04
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  5.2.4 MULTIPLE REGRESSION

  5.2.4 MULTIPLE REGRESSION

  • 46:41
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  5.2.5 MULTIPLE REGRESSION

  5.2.5 MULTIPLE REGRESSION

  • 38:14
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  5.2.6 MULTIPLE REGRESSION

  5.2.6 MULTIPLE REGRESSION

  • 37:48
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  5.2.7 MULTIPLE REGRESSION

  5.2.7 MULTIPLE REGRESSION

  • 1:01:26
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  5.3 KNN INTRO

  5.3 KNN INTRO

  • 26:49
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  5.3.1 KNN ALGORITHM

  5.3.1 KNN ALGORITHM

  • 48:57
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  5.3.2 KNN ALGORITHM

  5.3.2 KNN ALGORITHM

  • 11:17
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  5.3.3 INTRODUCTION TO CONFUSION MATRIX

  5.3.3 INTRODUCTION TO CONFUSION MATRIX

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

  5.3.4 INTRODUCTION TO SPLITTING THE DATASET USING TRAINTESTSPLIT

  • 24:37
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  5.3.5 KNN ALGORITHM

  5.3.5 KNN ALGORITHM

  • 50:29
• 

  5.3.6 KNN ALGORITHM

  5.3.6 KNN ALGORITHM

  • 56:10
• 

  5.4 INTRODUCTION TO DECISION TREE

  5.4 INTRODUCTION TO DECISION TREE

  • 44:37
• 

  5.4.1 INTRODUCTION TO DECISION TREE

  5.4.1 INTRODUCTION TO DECISION TREE

  • 39:32
• 

  5.4.2 DECISION TREE ALGORITHM

  5.4.2 DECISION TREE ALGORITHM

  • 36:41
• 

  5.4.3 DECISION TREE ALGORITHM

  5.4.3 DECISION TREE ALGORITHM

  • 20:10
• 

  5.4.4 DECISION TREE ALGORITHM

  5.4.4 DECISION TREE ALGORITHM

  • 55:37
• 

  5.5 UNSUPERVISED LEARNING

  5.5 UNSUPERVISED LEARNING

  • 23:26
• 

  5.5.1 UNSUPERVISED LEARNING

  5.5.1 UNSUPERVISED LEARNING

  • 09:16
• 

  5.5.2 UNSUPERVISED LEARNING

  5.5.2 UNSUPERVISED LEARNING

  • 18:28
• 

  5.5.3 UNSUPERVISED LEARNING

  5.5.3 UNSUPERVISED LEARNING

  • 29:50
• 

  5.5.4 AHC ALGORITHM

  5.5.4 AHC ALGORITHM

  • 46:30
• 

  5.5.5 AHC ALGORITHM

  5.5.5 AHC ALGORITHM

  • 19:55
• 

  5.6 KMEANS CLUSTERING

  5.6 KMEANS CLUSTERING

  • 23:08
• 

  5.6.1 KMEANS CLUSTERING

  5.6.1 KMEANS CLUSTERING

  • 30:25
• 

  5.6.2 KMEANS CLUSTERING

  5.6.2 KMEANS CLUSTERING

  • 1:01:04
• 

  5.6.3 DBSCAN ALGORITHM

  5.6.3 DBSCAN ALGORITHM

  • 37:09
• 

  5.6.4 DBSCAN PROGRAM

  5.6.4 DBSCAN PROGRAM

  • 32:45
• 

  5.6.5 DBSCAN PROGRAM

  5.6.5 DBSCAN PROGRAM

  • 49:56