MLCore : Python to ML [20 hours]

Pre-requisite : Fundamentals of Python

This isn't your typical "watch and forget" course. It's me and you – one-on-one – exploring the why and how of Machine Learning in a structured, yet chill way.

Session outline -

1. Intro to ML + Setting Expectations
2. Data Cleaning Like a Pro
3. EDA That Tells a Story
4. Feature Engineering Magic
5. Supervised vs Unsupervised Vibes
6. Linear Regression From Scratch
7. Logistic Regression – Not Just for Theory
8. Decision Trees: No More Black Boxes
9. Random Forests: The Cool Cousin
10. Model Evaluation Demystified
11. Overfitting? Let’s Tame It
12. Hyperparameter Tuning Zen
13. K-Means & Friends (Clustering)
14. PCA – Visualise, Don’t Memorise
15. Real World ML Project Planning
16. Scikit-learn Hands-On
17. Model Deployment 101
18. End-to-End Project: Idea to Insight
19. Portfolio Building Advice
20. Career & Interview Guidance

Please read here terms and conditions of the sessions.