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Machine Learning from Scratch

Master the full ML pipeline, from linear algebra, calculus, and probability fundamentals to implementing regression, classification, clustering, and neural networks from scratch in NumPy, then scaling to scikit-learn, PyTorch, and real-world datasets with proper train/val/test splits, cross-validation, and model evaluation metrics.

Created By

kishor

4 weeks

16 Learners

Mar 13

to start learning

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W1

Week 1: ML Fundamentals & Algorithms from Scratch

Week 1: ML Fundamentals & Algorithms from Scratch

By the end of this module you will be able to implement fundamental machine learning algorithms like Linear Regression, Logistic Regression, and K-Means clustering using only NumPy, demonstrating a deep understanding of their underlying mathematical principles.

7 videos

5 readings

7 topics

1 homework

Topics

1.1

Python for Data Science Essentials

Learn NUMPY in 5 minutes - BEST Python Library!

1.2

Linear Algebra Essentials

Vectors | Chapter 1, Essence of linear algebra

1.3

Calculus Essentials

Gradient descent simple explanation|gradient descent machine learning|gradient descent algorithm

1.4

Probability & Statistics Basics

Bayes theorem, the geometry of changing beliefs

1.5

Linear Regression from Scratch

Linear Regression, Cost Function and Gradient Descent Algorithm..Clearly Explained !!

1.6

Logistic Regression from Scratch

Lec-5: Logistic Regression with Simplest & Easiest Example | Machine Learning

1.7

K-Means Clustering from Scratch

StatQuest: K-means clustering

Required Reading & References

References

Week 1: Week 1: ML Fundamentals & Algorithms from Scratch

Week 2: Week 2: Scaling ML, Neural Networks & Real-World Applications

Week 3: Week 3: Advanced ML Techniques & Model Optimization

Week 4: Week 4: Specialized Domains & Deployment

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