Machine Learning

Machine Learning Specialization

Andrew Ng's updated three-course ML specialization — covering supervised learning, unsupervised learning, and reinforcement learning with modern Python tools. The most widely taken ML course in the world; the benchmark for team-wide ML onboarding.

Practical Deep Learning for Coders

Fast.ai's top-down approach to deep learning — start building real models immediately and learn theory as needed. Used by engineers transitioning into ML roles who benefit from hands-on practice over lectures. Free and actively maintained.

CS229: Machine Learning

Stanford's graduate-level ML course covering the mathematical foundations of supervised learning, Bayesian methods, SVMs, and unsupervised learning. The rigorous technical baseline for engineers who need to understand model behavior, not just call APIs.

An Introduction to Statistical Learning

The gold standard introduction to statistical learning — free in PDF, with Python and R labs. Covers linear regression, classification, resampling, regularization, and tree methods with mathematical rigor and practical examples. Required reading for ML practitioners.

Designing Machine Learning Systems

Chip Huyen's systems-level treatment of production ML — data management, feature engineering, model selection, deployment, and monitoring. Addresses reliability, scalability, and adaptability requirements for ML systems in regulated enterprise environments.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition

The most widely used practical ML book — covering the full stack from classical algorithms through deep neural networks. Updated for TensorFlow 2 and Keras. The benchmark reference for engineers implementing ML models in enterprise production environments.

But what is a neural network? (Deep Learning Chapter 1)

3Blue1Brown's iconic first video on neural networks — building intuition for how networks represent functions and learn from data with visual clarity unmatched in technical education. The starting point for non-engineers learning to think about ML.

Stanford CS229: Machine Learning (Full Course, Autumn 2018)

Andrew Ng's complete Stanford ML course — covering supervised learning, generative algorithms, SVMs, neural networks, and unsupervised learning with full mathematical derivations. The authoritative academic ML course available free on YouTube.

Neural Networks: Zero to Hero

Karpathy's complete series building neural networks from scratch in Python — from micrograd through character-level language models to a full GPT. The gold standard for technical leaders who want deep understanding of how modern ML systems work.

The Struggle Between Continuation and Refusal: A Mechanistic Analysis of the Continuation-Triggered Jailbreak in LLMs

Forecast Skill Is Not Decision Skill: Evidence from Weather-Dependent Decision Tasks

Learning from VAE Errors to support ECG-based Differential Diagnosis of Myocardial Scar

A Robust Watermark-based Fingerprint Framework for GNNs Ownership Verification

Locating and Steering Refusal Beyond Attention

Pause OpenAI, now

Flip, Don't Shuffle: Watermarking LLMs at the Speed of Inference

Pushing the (Decision) Boundaries: Dynamically Calibrating Differentially Private Noise to Explainability in Federated Learning

Extracting Forgotten Prompts from Targeted Unlearned Models

Inferred Generative-Process Diversity Predicts Correlated Failure Across Language Models

General Demographic Pre-trained Models for Enhancing Predictive Performance Across Diseases and Population

Untangling the Mechanisms of Misleading Context in Medical Question Answering

Kaggle

Google's data science platform providing free GPU compute, public datasets, competitions, and a library of community notebooks. The standard learning environment for ML practitioners who learn by doing — and a useful benchmark for team ML skill levels.

PyTorch

Facebook's open-source deep learning framework — now the dominant choice for both research and production ML. Dynamic computation graphs, strong ecosystem, and Hugging Face integration make it the default framework for modern neural network development.

scikit-learn

The most widely-used Python ML library — providing simple, consistent APIs for classification, regression, clustering, and model selection. The standard tool for ML practitioners implementing classical algorithms with production-grade reliability and documentation.