Machine Learning for Humans

[Usr6]

Who should read this?

• Technical people who want to get up to speed on machine learning quickly
• Non-technical people who want a primer on machine learning and are willing to engage with technical concepts
• Anyone who is curious about how machines think

This guide is intended to be accessible to anyone. Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn’t necessary to have prior knowledge of them to gain value from this series.

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.