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Linear regression in python

Track :

Computer Science

Course Presenter :

Learn With Jay

Lessons no : 6

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What will you learn in this course?
  • Implement linear regression models in Python using libraries like NumPy, Pandas, and Scikit-learn for real-world data analysis
  • Evaluate model performance with metrics such as R-squared and Mean Squared Error in Python-based linear regression projects
  • Visualize linear regression results and residuals using Matplotlib to interpret model accuracy and assumptions
  • Apply simple and multiple linear regression techniques to predict outcomes like house prices and sales trends
  • Identify and address issues like multicollinearity and heteroscedasticity in linear regression models using Python tools
  • Analyze datasets to understand relationships between variables and improve model robustness in Python environments

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Lessons | 6


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Dhanunjaya Sangu

this session is very helpful 2025-10-04

CH B V DURGA

Very good 2025-10-04

Dhanush Kumar. S

Good 2025-10-03

RENEE VINCENT ASR

Good 2025-09-17

Sher Azam

usefull 2025-09-08

Vivek Vk

good 2025-09-04

tijesunimi olorunnishola

This course was really helpful 2025-08-28

M.Nethra

good 2025-07-31

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Related Courses

Linear regression in python, in this course we will learn about Linear Regression in Python course. Linear regression is one of the most fundamental techniques in machine learning and statistics. This course provides a comprehensive introduction to linear regression, teaching you how to model the relationship between dependent and independent variables. You will start with the mathematical foundations, then implement linear regression models using Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn. You'll explore both simple and multiple linear regression, evaluate model performance using metrics like R-squared and Mean Squared Error, and visualize results. Real-world datasets will be used to apply your skills in practical scenarios, such as predicting house prices or analyzing trends. You’ll also learn about assumptions behind linear regression and how to detect issues like multicollinearity and heteroscedasticity. By the end of the course, you'll be able to confidently apply linear regression in Python to solve real analytical problems. Learn With Jay