Easy Explain

Machine Learning from Zero to Advanced | Shorts

A 30-episode visual course taking you from no background to advanced machine learning. Math (algebra, linear algebra, calculus, probability) is taught from scratch with animations and hand-worked numbers. Each algorithm is then derived, coded from scratch in Python, checked against scikit-learn or PyTorch, and evaluated honestly, ending with deep learning, Transformers, RL and production.

Shorts
19
Hours of video
0.3
Minutes of reading
19
Finished here
0
Start short Open on YouTube
#LessonVideoReadStatus
1Why Machine Learning Squares Its Mistakes (MSE by Hand)0:531 min
2A Dot Product Is Just Alignment, Explained Visually0:401 min
3Eigenvectors: The One Direction a Shear Can't Turn0:491 min
499% Accurate Test, Positive Result: Only 50% Sick?0:491 min
5Learning Rate: Too Small, Just Right, Too Big0:521 min
6Why Lasso Sets Weights Exactly to Zero (Diamond vs Circle)0:481 min
7Why 99% Accuracy Can Be Totally Useless0:561 min
8How AI Decides Which Words Matter: Attention Explained0:431 min
9Why Broadcasting Is NumPy's Superpower (and Its Silent Bug)0:551 min
10The 99% Accuracy That Was a Lie (Data Leakage Explained)0:531 min
11The Chain Rule in 50 Seconds: Gears, Rates and Backprop0:521 min
12Lagrange Multipliers in 50 Seconds: Where Gradients Line Up0:501 min
13Zero Correlation Does NOT Mean Independent (Proof in 50 Seconds)0:521 min
14Gradient Boosting: Just Keep Fitting the Mistakes0:531 min
15The Kernel Trick: Higher Dimensions for Free0:541 min
16Why Adam Works: Momentum Plus Scaling0:571 min
17Why Attention Divides by Square Root of d1:031 min
18LoRA Explained: Fine-Tune a Giant Model by Training a Sliver0:561 min
19The Bellman Equation: How a Gridworld Learns Its Values1:081 min