A Beginner’s Guide to AI/ML 🤖👶 – Machine Learning for Humans
The ultimate guide to machine learning. Simple, plain-English explanations accompanied by math, code, and real-world examples.
- Part 1: Why Machine Learning Matters. The big picture of artificial intelligence and machine learning — past, present, and future.
- Part 2.1: Supervised Learning. Learning with an answer key. Introducing linear regression, loss functions, overfitting, and gradient descent.
- Part 2.2: Supervised Learning II. Two methods of classification: logistic regression and SVMs.
- Part 2.3: Supervised Learning III. Non-parametric learners: k-nearest neighbors, decision trees, random forests. Introducing cross-validation, hyperparameter tuning, and ensemble models.
- Part 3: Unsupervised Learning. Clustering: k-means, hierarchical. Dimensionality reduction: principal components analysis (PCA), singular value decomposition (SVD).
- Part 4: Neural Networks & Deep Learning. Why, where, and how deep learning works. Drawing inspiration from the brain. Convolutional neural networks (CNNs), recurrent neural networks (RNNs). Real-world applications.
- Part 5: Reinforcement Learning. Exploration and exploitation. Markov decision processes. Q-learning, policy learning, and deep reinforcement learning. The value learning problem.
- Appendix: The Best Machine Learning Resources. A curated list of resources for creating your machine learning curriculum.