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The LION Way: Machine learning plus intelligent optimization.
Real-World Active Learning: Application and strategies for human-in-the-loop machine learning.
Machine Learning Logistics: Model Management in the Real World.
Interpretable Machine Learning: A guide for making black box models explainable.
A Year in Computer Vision.
Artificial Intelligence. Documentation artificial intelligence and machine learning topics
Serving Machine Learning Models. A guide to architecture, stream processing engines, and frameworks
Machine Learning.
Introduction to Machine Learning.
Algorithms for Reinforcement Learning.
Machine Learning - The Complete Guide.
Machine Learning, Neural and Statistical Classification.
Introduction to Machine Learning. Notes surveying many of the important topics in machine learning circa the late 1990s
Understanding Machine Learning: From Theory to Algorithms. An extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms
Statistical foundations of machine learning. Statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data
A Deep Learning Tutorial: From Perceptrons to Deep Networks.
Deep Learning for Natural Language Processing. A discussion of NLP-oriented issues in modeling, interpretation, representational power, and optimization
Unsupervised Feature Learning and Deep Learning.
Deep Learning: Methods and Applications. An overview of general deep learning methodology and its applications to a variety of signal and information processing tasks
Deep Learning. Resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular
Machine Learning by Andrew Ng. In this course, you'll learn about some of the most widely used and successful machine learning techniques
Neural Networks and Deep Learning. Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing
Unsupervised Feature Learning and Deep Learning.
Deep Learning Tutorial.
An Introduction to Unsupervised Learning via Scikit Learn.
Deep Learning by Samy Bengio, Tom Dean and Andrew Ng. Learn about widely used and successful machine learning techniques, with the opportunity to implement these algorithms
Practical Machine Learning: Innovations in Recommendations. Innovations that make machine learning practical for business production settings
Predictive Analytics for Dummies. Providing decision makers and analysts with the capability to make accurate predictions about future events based on complex statistical algorithms
Gaussian Processes for Machine Learning. One of the most important Bayesian machine learning approaches
Information Theory, Inference, and Learning Algorithms. Bayesian data modelling, Monte Carlo methods, variational methods, clustering algorithms, and neural networks
A First Encounter with Machine Learning. A first read to wet the appetite so to speak, a prelude to the more advanced machine learning topics
The Elements of Statistical Learning (Data Mining, Inference and Prediction). Bringing together many new ideas in learning and explaining them in a statistical framework
Introduction to Machine Learning. Very comprehensive overview of machine learning
Think Bayes: Bayesian Statistics Made Simple. Bayesian statistics with Python and discrete approximations
Computer Vision: Models, Learning, and Inference. A principled model-based approach to computer vision that unifies disparate algorithms, approaches, and topics under the guiding principles of probabilistic models, learning, and efficient inference algorithms
A Course in Machine Learning. Covers most major aspects of modern machine learning
Forecasting: principles and practice. Comprehensive introduction to forecasting methods
Bayesian Reasoning and Machine Learning. Unified treatment via graphical models, a marriage between graph and probability theory, facilitating the transference of Machine Learning concepts
Probabilistic Programming & Bayesian Methods for Hackers. Intro to Bayesian methods and probabilistic programming

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