Machine Learning from Zero to Advanced

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.

Lessons: 70
Hours of video: 16.7
Minutes of reading: 782
Finished here: 0

Start lesson 1 Open on YouTube

#	Lesson	Video	Read
1	What Is Machine Learning? A Visual Introduction for Beginners	5:32	5 min
2	Algebra Essentials for Machine Learning: Functions, Exponents, Logs and Sigma	6:39	6 min
3	Vectors and Dot Products: The Geometry Behind Every ML Prediction	4:03	3 min
4	Matrices as Transformations: See Linear Algebra Move	5:47	5 min
5	Eigenvectors, SVD and Positive-Definite Matrices Explained Visually	6:43	5 min
6	Derivatives From First Principles: Slopes, Rules and Why ML Needs Them	5:14	4 min
7	Partial Derivatives, Gradients, Jacobians and Hessians Explained Visually	6:24	5 min
8	Probability Basics and Bayes' Theorem: Why a 90% Accurate Test Can Be Wrong	5:53	5 min
9	Distributions, Expectation and the Central Limit Theorem Explained Visually	19:25	15 min
10	Maximum Likelihood, Entropy and Cross-Entropy Explained From Scratch	15:51	13 min
11	Loss Functions and Gradient Descent Explained Visually, With Numbers	17:24	14 min
12	Python and Data Foundations for ML: NumPy, Pandas, EDA and Data Leakage	16:53	13 min
13	Overfitting and the Bias-Variance Trade-off, Derived Step by Step	16:28	13 min
14	Linear Regression Explained: Normal Equation to Gradient Descent	16:42	13 min
15	Logistic Regression and Softmax: Derived from Scratch, Step by Step	19:23	16 min
16	Classification Metrics, Thresholds and Calibration Explained Visually	17:16	13 min
17	Regularization and Feature Engineering: Ridge, Lasso and Leak-Free Pipelines	18:54	15 min
18	k-Nearest Neighbors and Naive Bayes Explained: Distance, Bayes, Spam Filters	16:45	13 min
19	Decision Trees Explained: Entropy, Gini, Pruning and Code From Scratch	15:27	12 min
20	Random Forests, Boosting and SHAP Explained With Real Numbers	16:20	13 min
21	Support Vector Machines and Kernels Explained: Margins, Duals, RBF	18:11	15 min
22	Clustering Explained: k-Means, Hierarchical, DBSCAN and GMM	18:32	14 min
23	PCA, SVD, t-SNE and UMAP: Dimensionality Reduction Explained Visually	16:29	12 min
24	Model Evaluation and Selection Done Right: CV, Tuning, Leakage	13:46	10 min
25	Time Series and Forecasting: ARIMA, Smoothing and Leakage Explained	19:39	16 min
26	Neural Networks From Scratch: Backpropagation Derived Step by Step	17:54	14 min
27	PyTorch and Convolutional Neural Networks: Tensors to Transfer Learning	19:42	15 min
28	RNNs, LSTMs and Attention Explained With Real Numbers	16:57	14 min
29	Transformers Explained: Attention, Math and How LLMs Really Work	16:59	13 min
30	Reinforcement Learning, Causal Inference and MLOps: The ML Capstone	16:24	13 min
31	NumPy Arrays, Shapes and Broadcasting Explained Visually (ML Lesson 31)	12:50	10 min
32	Pandas Essentials for Machine Learning Data (Lesson 32)	12:38	9 min
33	Plotting and EDA: Look at Your Data Before You Model It	13:12	10 min
34	Data Cleaning for ML: Missing Values, Outliers and Feature Types	13:41	11 min
35	Why Your 99% Model Is a Lie: Leakage-Safe Splits and Pipelines	12:31	10 min
36	The Chain Rule and Computational Graphs: How Gradients Really Flow	17:01	14 min
37	Convexity and Lagrange Multipliers: Optimization With Rules (ML Lesson 37)	14:02	11 min
38	Covariance, Correlation and the Multivariate Gaussian Explained	15:37	12 min
39	Confidence Intervals and Hypothesis Tests for ML: Is Your Model Really Better?	13:17	11 min
40	Linear Regression From Scratch in NumPy: Assumptions and Residual Analysis	13:01	10 min
41	Logistic Regression Gradient Derived From Scratch (Full Math + NumPy)	14:56	12 min
42	Gradient Boosting and XGBoost: The Math, Derived Step by Step	14:52	12 min
43	The SVM Dual and the Kernel Trick: The Math Explained Step by Step	15:42	13 min
44	Expectation-Maximization for Gaussian Mixtures: The Full Math and Code	16:10	13 min
45	Hyperparameter Search and Bayesian Optimization Explained With Numbers	14:26	11 min
46	ARIMA and Exponential Smoothing Explained With Real Numbers	15:48	12 min
47	Weight Initialization, Vanishing Gradients and Optimizers: Why Deep Nets Train	14:55	11 min
48	Dropout and Batch Normalization Explained With Numbers and NumPy Code	16:11	13 min
49	PyTorch Tensors and Autograd: Gradients You Can Verify by Hand	14:33	11 min
50	PyTorch Training Loops, DataLoaders, GPUs and Debugging Explained	12:54	10 min
51	Convolution Explained: Kernels, Stride, Padding and Pooling by Hand	13:59	11 min
52	CNN Architectures: LeNet, ResNet, Transfer Learning and Grad-CAM Explained	14:41	12 min
53	LSTM, GRU and Backpropagation Through Time Explained With Numbers	15:12	12 min
54	Tokenization and Embeddings: How Text Becomes Numbers (BPE by Hand)	16:02	13 min
55	Attention Explained With Real Numbers: Scaled Dot-Product and Multi-Head	13:26	10 min
56	The Full Transformer Block: Residuals, LayerNorm, GPT vs BERT vs T5	15:48	12 min
57	Pretraining, Fine-Tuning and LoRA Explained With Real Numbers	15:50	12 min
58	How LLMs Actually Run: Decoding, KV Cache, Latency and Limits	13:40	10 min
59	Reinforcement Learning I: MDPs, Bellman Equations, DP and Monte Carlo	14:52	12 min
60	Reinforcement Learning II: TD, Q-Learning, Policy Gradients, DQN and PPO	17:10	13 min
61	Bayesian ML: MAP, Priors, and Gaussian Processes Explained	14:41	11 min
62	Trustworthy ML: Uncertainty, Shift, Fairness and Robustness Explained	14:29	11 min
63	MLOps: Reproducible Pipelines, Serving, Deployment and Monitoring	13:36	10 min
64	Two Full ML Projects: House Prices and Customer Churn, Start to Finish	11:26	9 min
65	Credit Risk, Fairness and Anomaly Detection: Capstone 3 and 4	10:57	8 min
66	Spam Filter and Text Classifier From Scratch: Naive Bayes to Transformers	12:43	10 min
67	Customer Segmentation and a Recommender From Scratch (Capstone 7-8)	14:52	12 min
68	Capstone: Train a CNN and a Tiny GPT From Scratch (Grad-CAM, Perplexity)	14:21	11 min
69	Forecasting to Production: Time Series, FastAPI, Docker and Drift Monitoring	12:58	10 min
70	Machine Learning Course Wrap-Up: The Full Map and Your Roadmap Beyond	13:38	10 min