Machine Learning from Zero to Hero 2024
In this video, we explore the fundamentals of linear regression—and how to implement regression linear in Python using scikit-Learn with an example. Linear regression helps us understand relationships between variables by fitting a straight line to our data.
We'll break down the theory behind linear regression, explore key concepts like the line of best fit, and walk through the process of calculating the slope and intercept. By the end of this video, you'll understand how to apply linear regression to real-world data and interpret the results.
Whether you're new to machine learning or looking to solidify your understanding, this video provides a clear and practical explanation of linear regression. Don’t forget to check out the additional resources and the code on my GitHub, linked in the description below. ⬇️
📚 Video Chapters:
0:00 - Introduction to Linear Regression
1:52 - What is Linear Regression?
3:33 - The Line of Best Fit
4:26 - Mean Squared Error
5:13 - Implementing Linear Regression in Python with Sklearn
11:18 - Conclusion and Next Steps
#MachineLearning #LinearRegression #Python #Sklearn #DataScience #AI #SupervisedLearning #MLTutorial
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