Machine Learning Basics in Hindi हिंदी - Regression Analysis

हिंदी में सीखें Basics of Machine Learning - covers Simple Linear Regression & Multiple Linear regression in Python

4.12 (343 reviews)
Data Science
Machine Learning Basics in Hindi हिंदी - Regression Analysis
5.5 hours
Nov 2023
last update
regular price

What you will learn

Linear Regression technique का उपयोग करके real life problem को हल करना सीखें

Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression

Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm

How to convert business problem into a Machine learning Linear Regression problem


आप एक पूर्ण Linear Regression course की तलाश कर रहे हैं जो आपको वह सब कुछ सिखाता है जो आपको Python में Linear Regression model बनाने के लिए चाहिए, है ना?

आपको सही Linear Regression course मिल गया है!

After completing this course you will be able to:

  • Identify the business problem which can be solved using linear regression technique of Machine Learning.

  • Create a linear regression model in Python and analyze its result.

  • Confidently practice, discuss and understand Machine Learning concepts

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

How this course will help you?

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear Regression

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through linear regression.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.

Below are the course contents of this course on Linear Regression:

  • Section 1 - Basics of Statistics

    This section is divided into five different lectures starting from types of data then types of statistics

    then graphical representations to describe the data and then a lecture on measures of center like mean

    median and mode and lastly measures of dispersion like range and standard deviation

  • Section 2 - Python basic

    This section gets you started with Python.

    This section will help you set up the python and Jupyter environment on your system and it'll teach

    you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

  • Section 3 - Introduction to Machine Learning

    In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

  • Section 4 - Data Preprocessing

    In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.

    We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.

  • Section 5 - Regression Model

    This section starts with simple linear regression and then covers multiple linear regression.

    We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

    We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.

By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.

Go ahead and click the enroll button, and I'll see you in lesson 1!


Start-Tech Academy


Below is a list of popular FAQs of students who want to start their Machine learning journey-

What is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

What is the Linear regression technique of Machine learning?

Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value.

Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).

When there is a single input variable (x), the method is referred to as simple linear regression.

When there are multiple input variables, the method is known as multiple linear regression.

Why learn Linear regression technique of Machine learning?

There are four reasons to learn Linear regression technique of Machine learning:

1. Linear Regression is the most popular machine learning technique

2. Linear Regression has fairly good prediction accuracy

3. Linear Regression is simple to implement and easy to interpret

4. It gives you a firm base to start learning other advanced techniques of Machine Learning

How much time does it take to learn Linear regression technique of machine learning?

Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.

Why use Python for data Machine Learning?

Understanding Python is one of the valuable skills needed for a career in Machine Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

    In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.



Welcome to the course!
Course contents
Course Resources

Setting up Python and Jupyter Notebook

This is a milestone!
Introduction to Google Colab
Arithmetic operators in Python: Python Basics
Strings in Python: Python Basics
Lists, Tuples and Directories: Python Basics
Working with Numpy Library of Python
Working with Pandas Library of Python
Working with Seaborn Library of Python

Basics of Statistics

Types of Data
Types of Statistics
Describing data Graphically
Measures of Centers
Measures of Dispersion

Introduction to Machine Learning

Introduction to Machine Learning

Data Preprocessing

Gathering Business Knowledge
Data Exploration
The Dataset and the Data Dictionary
Importing Data in Python
Univariate analysis and EDD
EDD in Python
Outlier Treatment
Outlier Treatment in Python
Missing Value Imputation
Missing Value Imputation in Python
Seasonality in Data
Bi-variate analysis and Variable transformation
Variable transformation and deletion in Python
Non-usable variables
Dummy variable creation: Handling qualitative data
Dummy variable creation in Python
Correlation Analysis
Correlation Analysis in Python

Linear Regression

The Problem Statement
Basic Equations and Ordinary Least Squares (OLS) method
Assessing accuracy of predicted coefficients
Assessing Model Accuracy: RSE and R squared
Simple Linear Regression in Python
Multiple Linear Regression
The F - statistic
Interpreting results of Categorical variables
Multiple Linear Regression in Python
Test-train split
Bias Variance trade-off
Test train split in Python
Regression models other than OLS
Subset selection techniques
Shrinkage methods: Ridge and Lasso
Ridge and Lasso in Python

Congratulations & About your certificate

The final milestone!
Bonus lecture


March 24, 2023
it was really very good experience to learning this course, all concepts are explaining very deeply and in well manner, i Really Enjoy this course, thanks to creator of this course for sharing thier knowldge..
November 24, 2022
Really great explanation. Though the coding part was limited but the theoretical concepts were covered really well. Great course to learn basics of ml.
November 10, 2022
As a beginner in machine learning/ AI technology, I learned the basic concepts from this course, which was extremely helpful.
October 2, 2022
I am very much passionate about Machine Learning and Deep Learning and will do definitely complete this course successful.


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