Advanced Machine Learning
edX
Columbia University's survey course on edX, presented with the underlying mathematics rather than library calls. Works through regression, maximum likelihood and Bayesian estimation, classification, kernel methods, support vector machines, boosting, clustering, expectation maximisation, hidden Markov models, and matrix factorisation.
More resources on Machine Learning
Practical Deep Learning for Coders
fast.ai's free course teaching deep learning top-down: you train working image, text, and tabular models in the first lessons, then work back to the underlying mechanics. Assumes about a year of coding experience, uses PyTorch and the fastai library.
Distill.pub
Peer-reviewed web journal of machine learning explanations, publishing interactive articles on topics like feature visualisation, attention and neural network interpretability. Archive remains readable, though the journal went on indefinite hiatus in 2021 and no longer publishes.
The TWIML AI Podcast
Long-running interview podcast hosted by Sam Charrington in which ML and AI researchers and practitioners discuss their work on deep learning, natural language processing, neural networks and data science, helping listeners follow current research and how it is applied in industry.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd Edition)
The standard reference for the TensorFlow/Keras side of deep learning, moving from scikit-learn regression and ensembles into Keras APIs, custom layers, tf.data, training loops, CNNs, transformers, and TFX deployment. Notebooks for every chapter are on GitHub.
Deep Learning
A seminal review paper by three pioneers of deep learning, providing an overview of the field, its history, key concepts, and future directions. It's an excellent read for understanding the landscape of deep learning.
StatQuest with Josh Starmer - Neural Networks videos
Josh Starmer's StatQuest provides clear, concise, and often humorous explanations of statistical and machine learning concepts. His videos on neural networks break down complex ideas into easily digestible 'quests.'