Creating Linear Regression Model for Machine Learning in Hindi | Shiv Kumar | Skillshare

# Creating Linear Regression Model for Machine Learning in Hindi

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11 Lessons (54m)

1:14

10:47

8:46

5:11

5:45

7:22

5:19

3:43

6:32

12:27
• ### 11. Project Work Finding Regression Line and Conclusion

6:23
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In this class , I will teach about building Linear Regression Model in Machine Learning for prediction problems. Linear Regression is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope. It's used to predict values within a continuous range, (e.g. sales, price) rather than trying to classify them into categories (e.g. cat, dog).

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.

And, linear regression is one of the most popular regression technique used in machine learning.

In this class I will cover the topics:

• Introduction
• What is Linear Regression?
• Python numpy sklearn
• Steps in Creating Linear Regression Model
• Implementing Linear Regression for Prediction
• Project Work Finding Regression Line and Conclusion

After finishing this class, students will be able to work on machine learning problems which requires Linear Regression model for prediction.

Who should join this class:

• Students aspire to become Data Scientist
• Software Engineers
• Data Analysts
• Junior Data Scientists
• Anyone aspire to learn machine learning and its models

Pre-requisite for the class:

• Basic Understanding of Python Programming
• Basics of Machine Learning and Data Science
• Interested to be part of Data Science Ecosystem

#### Shiv Kumar

Programmer & Data Scientist

Teacher

Hello, I'm Shiv Kumar. Programmer by profession and also by nature. I have worked for more than 10 years in IT and software industry. I am here to share my expertise and knowledge.

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## Related Skills

Technology Python Data Science Machine Learning Linear Regression

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