𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗠𝗟 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝘄𝗶𝘁𝗵 𝗮 𝗦𝗼𝗹𝗶𝗱 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻

𝗟𝗶𝗻𝗲𝗮𝗿 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 is often the first algorithm every beginner encounters in the 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 𝗼𝗳 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴. But simply understanding the gradient function isn't enough—building a strong foundation requires an in-depth study of the interconnected concepts.

To help you get started, here’s a comprehensive series of lectures designed to make your ML fundamentals robust. Delivered in Hindi and explained on a whiteboard—𝘫𝘶𝘴𝘵 𝘭𝘪𝘬𝘦 𝘶𝘯𝘪𝘷𝘦𝘳𝘴𝘪𝘵𝘺 𝘤𝘭𝘢𝘴𝘴𝘳𝘰𝘰𝘮𝘴—these lectures provide a structured, deep-dive approach to learning:

1. Quartile & Box Plot:

2. Loss function and Gradient descent:

3. Concept of linear regression and R2 score:

4. Assumptions of Linear Regression:

5. Multicollinearity and VIF:

6. Polynomial regression:

7. L1 L2 Regularization:

8. Hyoeroarameter Tuning:

9. K-Fold cross validation:

10. Encoding categorical variable:

11. Interview preparation:

12. End-to-end project:

by Pritam Kudale

🎥 Each lecture is 45 minutes to 1 hour long and dives deep into the concepts to strengthen your ML foundation.

This series is just the beginning! Upcoming videos will cover classification, clustering, natural language processing, and more advanced topics.

💡 Remember: Learning Machine Learning and AI should never be limited by language barriers.

Dive into this lecture series to make your ML fundamentals unshakable. Let’s build a strong foundation for your AI journey together!

#LinearRegression #MachineLearning #DataScience #AIInHindi #MLBasics #LearningJourney