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Regression Analysis in Hindi
4.5
Beginner level
3.75 learning hrs
Strengthen your base in Predictive Analysis and Machine Learning with Regression Analysis in Hindi
Instructor:
Mr. Bharani Akella
Modules updated 07/2025
About this course
Regression algorithms are very important when it comes to predictive modeling or machine learning. We have multiple types of regression analysis such as linear regression, logistic regression and Poisson regression. In this course, we will start off by learning the fundamentals of data science and then look at the data science life cycle. After that, we will learn the concepts of statistics which are required for machine learning. Going ahead, We will understand the need of regression analysis in hindi and start off with our first regression algorithm which is linear regression. After we understand the theoretical concepts of regression, we will have a comprehensive demo on linear regression. Following which we will learn about logistic regression and also have a demo on classification with logistic regression.
Course outline
Introduction to Hive Hands-On
Hive is a data warehouse used to support interaction between the user and HDFS. This course will give you a demonstration using sample problem statements for your better understanding.
Statistics
Data Science Life Cycle
Data Science life cycle revolves around data acquisition, preprocessing, ML algorithms, pattern evaluation, knowledge representation, and analytical strategies to predict and proffer insights, which we shall learn in this module, into the procedures to yield the best results.
Linear Regression Theory
Linear Regression Demo
This section shall explain simple linear regression. You will learn to import classes and packages and work with Google Colaboratory to understand linear regression better.
Logistic Regression
This section explains logistic regression, its advantages and disadvantages, concordance, and difference between logit and logistic regression. Later you will understand the term error, collinearity, predictive modelling, and the importance of logistic regression. You will also understand to evaluate a logistic regression model.
Logistic Regression Demo
Get access to the complete curriculum once you enroll in the course
4.5
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Skill & tools
100% of learners found all the desired skills & tools
Our course instructor
Mr. Bharani Akella
Data Scientist
Frequently Asked Questions
Will I receive a certificate upon completing this free course?
Is this course free?
What is Regression Analysis with examples?
Regression analysis indicates an applied mathematics method (Statistical Process) for estimating the relationships between variables. It can be used to predict future values of the dependent variable based on the values of the independent variable. Some of the examples of Regression Analysis include Sales prediction, Weather prediction, Cost prediction, etc.
What is the Data Science Lifecycle?
A Data Science lifecycle consists of the iterative set of several Data Science steps you follow to deliver an analysis or a project. The Data Science life cycles differ from one another as every project is different. You can learn the Data Science lifecycle in detail through Great Learning’s free Regression Analysis in Hindi course.
What are the uses of Regression Analysis?
Regression Analysis has many uses. Some typical applications include:
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Estimating the relationships between variables
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Making Predictions
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Testing Hypothesis
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Understanding cause and effect
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