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)](https://www.edx.org/course/artificial-intelligence-ai-columbiax-csmm-101x-0) - free
* [Machine Learning (Columbia University)](https://www.edx.org/course/machine-learning-columbiax-csmm-102x-0) - free
* [Machine Learning (Stanford University)](https://www.coursera.org/learn/machine-learning) - free
* [Deep Learning Specialization (by Andrew Ng, deeplearning.ai)](https://www.coursera.org/specializations/deep-learning) - 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](https://www.udacity.com/course/deep-learning-nanodegree--nd101) - $
* [Intro to Deep Learning (MIT)](http://introtodeeplearning.com/)
* [Stanford's CS20 Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/)
* [fast.ai](https://www.fast.ai/) - deep learning MOOC
* [Full-Stack Deep Learning](https://fullstackdeeplearning.com/) 
* [Amazon's MLU-Explain](https://mlu-explain.github.io/) - Visual, Interactive Explanations of Core Machine Learning Concepts
* [Machine Learning Specialization (University of Washington)](https://www.coursera.org/specializations/machine-learning) - 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)](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) - Lecture slides and video recordings.
* [Learning from Data (by Yaser S. Abu-Mostafa, Caltech)](http://www.work.caltech.edu/telecourse.html) - Lecture videos available
* [Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120) - free
* [Probabilistic Graphical Models (by Prof. Daphne Koller, Stanford)](https://www.coursera.org/specializations/probabilistic-graphical-models) Coursera Specialization
* [Reinforcement Learning Course (by David Silver, DeepMind)](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLzuuYNsE1EZAXYR4FJ75jcJseBmo4KQ9-) - YouTube playlist and [lecture slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html).
* [Keras in Motion](https://www.manning.com/livevideo/keras-in-motion) $
* [Stanford's CS231n: CNNs for Visual Recognition](https://www.youtube.com/watch?v=vT1JzLTH4G4&index=1&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) - Spring 2017 iteration, instructors (Fei-Fei Li, Justin Johnson, Serena Yeung), or [Winter 2016 edition](https://www.youtube.com/watch?v=NfnWJUyUJYU&list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC) instructors (Fei-Fei Li, Andrej Karpathy, Justin Johnson). [Course website](http://cs231n.github.io/) has supporting material.
* [University of California, Berkeley's CS294: Deep Reinforcement Learning](https://www.youtube.com/watch?v=8jQIKgTzQd4&list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX) - Fall 2017 edition. [Course website](http://rll.berkeley.edu/deeprlcourse/) has lecture slides and other related material.
* [Machine Learning (Georgia Tech) on Udacity](https://www.udacity.com/course/machine-learning--ud262) - free
* [Reinforcement Learning (Georgia Tech) on Udacity ](https://www.udacity.com/course/reinforcement-learning--ud600) - free
* [Machine Learning for Trading](https://www.udacity.com/course/machine-learning-for-trading--ud501) - free
* [Mining of Massive Datasets](https://www.youtube.com/watch?v=xoA5v9AO7S0&list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV) (YouTube playlist) - Course [website](http://mmds.org/) has info about accompanying book, free chapters, and Stanford's [MOOC](https://lagunita.stanford.edu/courses/course-v1:ComputerScience+MMDS+SelfPaced/about)
* [Machine Learning Crash Course (Google)](https://developers.google.com/machine-learning/crash-course/) - free
* [Machine Learning Mini Bootcamp Course (LambdaSchool)](https://lambdaschool.com/courses/data-science/intro/) - free and $
* [Microsoft Professional Program for Artificial Intelligence](https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/) - free 
* [Open Machine Learning Course](https://github.com/Yorko/mlcourse.ai) with [articles](https://medium.com/open-machine-learning-course) on Medium 
* [Machine Learning A-Z (Udemy)](https://www.udemy.com/machinelearning/) - Hands-On Python & R In Data Science
* [Deep Learning Crash Course](https://www.manning.com/livevideo/deep-learning-crash-course) - $
* [Reinforcement Learning in Motion](https://www.manning.com/livevideo/reinforcement-learning-in-motion) - $
* [Udemy A-Z Machine learning course](https://www.udemy.com/course/machinelearning/) - $
* [Statistics and Probability-Khan Academy](https://www.khanacademy.org/math/statistics-probability) - free
* [Math and Architectures of Deep Learning](https://www.manning.com/books/math-and-architectures-of-deep-learning) - $
* [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition) - $
* [Transfer Learning for Natural Language Processing](https://www.manning.com/books/transfer-learning-for-natural-language-processing) - $
* [Grokking Artificial Intelligence Algorithms](https://www.manning.com/books/grokking-artificial-intelligence-algorithms) - $
* [Learn ML from experts at Google](https://ai.google/education/) - free
* [Kaggle courses on ML,AI and DS(certificate)](https://www.kaggle.com/learn/overview) - free
* [Ml with python(Cognitive classes)](https://cognitiveclass.ai/courses/machine-learning-with-python) - free
* [Intro to Data science(Cognitive classes)](https://cognitiveclass.ai/courses/data-science-101) - free
* [Machine Learning for Business](https://www.manning.com/books/machine-learning-for-business) - $
* [Transfer Learning for Natural Language Processing](https://www.manning.com/books/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)](https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) - free
* [Data Scientist in Python (Dataquest)](https://www.dataquest.io/path/data-scientist/) - free and $
* [AI Expert Roadmap - Roadmap to becoming an Artificial Intelligence Expert](https://github.com/AMAI-GmbH/AI-Expert-Roadmap) - free
* [Semi-Supervised Deep Learning with GANs for Melanoma Detection](https://www.manning.com/liveproject/semi-supervised-deep-learning-with-gans-for-melanoma-detection) - $
* [Interpretable AI](https://www.manning.com/books/interpretable-ai) - $
* [Deploying a Deep Learning Model on Web and Mobile Applications Using TensorFlow](https://www.manning.com/liveproject/deploying-a-deep-learning-model-on-web-and-mobile-applications-using-tensorflow) - $ Hands-on project
* [Complete Data Science and ML Course](https://www.scaler.com/data-science-course/) - $
* [ML Observability Fundamentals](https://arize.com/ml-observability-fundamentals/) - free
* [Introduction to Data-Centric AI (MIT)](https://dcai.csail.mit.edu/) - free
* [Data science course with placement](https://brainalyst.in/data-science-course-placement-guarantee)
* [DATA VISUALIZATION COURSE](https://brainalyst.in/data-visualization-courses-online/)
* [DATA VISUALIZATION PYTHON COURSE](https://brainalyst.in/data-visualization-python/)
* [DATA SCIENCE WITH R PROGRAMMING](https://brainalyst.in/data-science-with-r/)
* [DATA SCIENCE WITH PYTHON](https://brainalyst.in/data-science-with-python-course/)
* [DATA SCIENCE 360 TRAINING COURSE](https://brainalyst.in/data-science-360-training-course/)
* [BIG DATA & CLOUD COMPUTING COURSE](https://brainalyst.in/big-data-cloud-computing-courses/)
* [FULL STACK DATA SCIENCE PROGRAM](https://brainalyst.in/full-stack-data-science-course-program/)
* [Mathematics for Machine Learning Specialization (Imperial College London via Coursera)](https://www.coursera.org/specializations/mathematics-machine-learning?action=enroll) - $ but financial aid available
* [Machine Learning Engineering for Production (MLOps) Specialization (DeepLearning.ai)](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops) - $ but financial aid available
* [LLMOps: Building Real-World Applications With Large Language Models](https://www.comet.com/site/llm-course/) - free