Machine Learning
Andrew Ng
In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
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.'