Regression Analysis in Hindi

Strengthen your base in Predictive Analysis and Machine Learning with Regression Analysis in Hindi

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Beginner

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3.75 Hrs

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2.5K+

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Regression Analysis in Hindi

3.75 Learning Hours . Beginner

Skills you’ll Learn

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.

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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

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Earn a certificate of completion

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Regression Analysis in Hindi

3.75 Learning Hours . Beginner

Frequently Asked Questions

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:

  • Estimating the relationships between variables

  • Making Predictions

  • Testing Hypothesis

  • Understanding cause and effect

What are the different types of Regression Analysis?

The types of Regression Analysis include:

  • Logistic

  • Stepwise

  • Hierarchical

You can gain in-depth knowledge on these types of Regression Analysis through the Regression Analysis in Hindi course Great Learning offers.

 

Will I get a certificate after completing this Regression Analysis in Hindi free course?

Yes, you will get a certificate of completion for Regression Analysis in Hindi after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.
 

How much does this Regression Analysis in Hindi course cost?

It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of Regression Analysis in Hindi can get started with this course.
 

Is there any limit on how many times I can take this free course?

Once you enroll in the Regression Analysis in Hindi course, you have lifetime access to it. So, you can log in anytime and learn it for free online.
 

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.

Why choose Great Learning Academy for this free Regression Analysis in Hindi course?

Great Learning Academy provides this Regression Analysis in Hindi course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts.

Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.
 

What are the steps to enroll in this Regression Analysis in Hindi course?

Enrolling in any of the Great Learning Academy’s courses is just one step process. Sign-up for the course, you are interested in learning through your E-mail ID and start learning them for free online.
 

Will I have lifetime access to this free Regression Analysis in Hindi course?

Yes, once you enroll in the course, you will have lifetime access, where you can log in and learn whenever you want to. 
 

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