machine-learning/courses.md

The following is a list of free or paid online courses on machine learning, statistics, data-mining, etc.

Machine-Learning / Data Mining

• Artificial Intelligence (Columbia University) - free
• Machine Learning (Columbia University) - free
• Machine Learning (Stanford University) - free
• Neural Networks for Machine Learning (University of Toronto) - free. Also available on YouTube as a playlist. #This course is no longer available on Coursera.
• Deep Learning Specialization (by Andrew Ng, deeplearning.ai) - Courses: I Neural Networks and Deep Learning; II Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; III Structuring Machine Learning Projects; IV Convolutional Neural Networks; V Sequence Models; Paid for grading/certification, financial aid available, free to audit
• Deep Learning Nano Degree on Udacity - $
• Intro to Deep Learning (MIT)
• Stanford's CS20 Tensorflow for Deep Learning Research
• fast.ai - deep learning MOOC
• Full-Stack Deep Learning
• Amazon's MLU-Explain - Visual, Interactive Explanations of Core Machine Learning Concepts
• Machine Learning Specialization (University of Washington) - Courses: Machine Learning Foundations: A Case Study Approach, Machine Learning: Regression, Machine Learning: Classification, Machine Learning: Clustering & Retrieval, Machine Learning: Recommender Systems & Dimensionality Reduction,Machine Learning Capstone: An Intelligent Application with Deep Learning; free
• Machine Learning Course (2014-15 session) (by Nando de Freitas, University of Oxford) - Lecture slides and video recordings.
• Learning from Data (by Yaser S. Abu-Mostafa, Caltech) - Lecture videos available
• Intro to Machine Learning - free
• Probabilistic Graphical Models (by Prof. Daphne Koller, Stanford) Coursera Specialization
• Reinforcement Learning Course (by David Silver, DeepMind) - YouTube playlist and lecture slides.
• Keras in Motion $
• Stanford's CS231n: CNNs for Visual Recognition - Spring 2017 iteration, instructors (Fei-Fei Li, Justin Johnson, Serena Yeung), or Winter 2016 edition instructors (Fei-Fei Li, Andrej Karpathy, Justin Johnson). Course website has supporting material.
• University of California, Berkeley's CS294: Deep Reinforcement Learning - Fall 2017 edition. Course website has lecture slides and other related material.
• Machine Learning (Georgia Tech) on Udacity - free
• Reinforcement Learning (Georgia Tech) on Udacity - free
• Machine Learning for Trading - free
• Mining of Massive Datasets (YouTube playlist) - Course website has info about accompanying book, free chapters, and Stanford's MOOC
• Machine Learning Crash Course (Google) - free
• Machine Learning Mini Bootcamp Course (LambdaSchool) - free and $
• Microsoft Professional Program for Artificial Intelligence - free
• Open Machine Learning Course with articles on Medium
• Machine Learning A-Z (Udemy) - Hands-On Python & R In Data Science
• Deep Learning Crash Course - $
• Reinforcement Learning in Motion - $
• Udemy A-Z Machine learning course - $
• Statistics and Probability-Khan Academy - free
• Math and Architectures of Deep Learning - $
• Deep Learning with Python, Second Edition - $
• Transfer Learning for Natural Language Processing - $
• Grokking Artificial Intelligence Algorithms - $
• Learn ML from experts at Google - free
• Kaggle courses on ML,AI and DS(certificate) - free
• Ml with python(Cognitive classes) - free
• Intro to Data science(Cognitive classes) - free
• Machine Learning for Business - $
• Transfer Learning for Natural Language Processing - $
• In-depth introduction to machine learning in 15 hours of expert videos (by Prof. Trevor Hastie, Prof. Rob Tibshirani, Stanford) - free
• Data Scientist in Python (Dataquest) - free and $
• AI Expert Roadmap - Roadmap to becoming an Artificial Intelligence Expert - free
• Semi-Supervised Deep Learning with GANs for Melanoma Detection - $
• Interpretable AI - $
• Deploying a Deep Learning Model on Web and Mobile Applications Using TensorFlow - $ Hands-on project
• Complete Data Science and ML Course - $
• ML Observability Fundamentals - free