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.

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.

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.

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.

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.

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.

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 .

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

In this tutorial we learn about implementation of Agglomerates hierarchical clusters in machine learning and also talk about features of hierarchical clusters.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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( ).

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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

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.

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.

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.

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

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.

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

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.