AI / Machine Learning / Deep Learning online courses

Many people ask me on a daily basis how to switch to Data Science. I feel I should write a good post on that but by that time sharing the list of online courses from decent institutes.

You can not afford to go to Stanford or Berkely or attend Andrew Ng's lecture at Stanford.

But now it has been so easy to learn from them seating at your own desk.

Artificial Intelligence

• CS50 - Introduction to Artificial Intelligence with Python (and Machine Learning), Harvard OCW
• CS 188 - Introduction to Artificial Intelligence, UC Berkeley - Spring 2015
• 6.034 Artificial Intelligence, MIT OCW
• CS221: Artificial Intelligence: Principles and Techniques - Autumn 2019 - Stanford University
• 15-780 - Graduate Artificial Intelligence, Spring 14, CMU
• CSE 592 Applications of Artificial Intelligence, Winter 2003 - University of Washington
• CS322 - Introduction to Artificial Intelligence, Winter 2012-13 - UBC (YouTube)
• CS 4804: Introduction to Artificial Intelligence, Fall 2016
• CS 5804: Introduction to Artificial Intelligence, Spring 2015
• Artificial Intelligence - IIT Kharagpur
• Artificial Intelligence - IIT Madras
• Artificial Intelligence(Prof.P.Dasgupta) - IIT Kharagpur
• MOOC - Intro to Artificial Intelligence - Udacity
• MOOC - Artificial Intelligence for Robotics - Udacity
• Graduate Course in Artificial Intelligence, Autumn 2012 - University of Washington
• Agent-Based Systems 2015/16- University of Edinburgh
• Informatics 2D - Reasoning and Agents 2014/15- University of Edinburgh
• Artificial Intelligence - Hochschule Ravensburg-Weingarten
• Deductive Databases and Knowledge-Based Systems - Technische Universität Braunschweig, Germany
• Artificial Intelligence: Knowledge Representation and Reasoning - IIT Madras
• Semantic Web Technologies by Dr. Harald Sack - HPI
• Knowledge Engineering with Semantic Web Technologies by Dr. Harald Sack - HPI
• T81-558: Applications of Deep Neural Networks by Jeff Heaton, 2022, Washington University in St. Louis

Machine Learning

• Introduction to Machine Learning
• MOOC Machine Learning Andrew Ng - Coursera/Stanford (Notes)
• Introduction to Machine Learning for Coders
• MOOC - Statistical Learning, Stanford University
• Foundations of Machine Learning Boot Camp, Berkeley Simons Institute
• CS155 - Machine Learning & Data Mining, 2017 - Caltech (Notes) (2016)
• CS 156 - Learning from Data, Caltech
• 10-601 - Introduction to Machine Learning (MS) - Tom Mitchell - 2015, CMU (YouTube)
• 10-601 Machine Learning | CMU | Fall 2017
• 10-701 - Introduction to Machine Learning (PhD) - Tom Mitchell, Spring 2011, CMU (Fall 2014) (Spring 2015 by Alex Smola)
• 10 - 301/601 - Introduction to Machine Learning - Spring 2020 - CMU
• CMS 165 Foundations of Machine Learning and Statistical Inference - 2020 - Caltech
• Microsoft Research - Machine Learning Course
• CS 446 - Machine Learning, Spring 2019, UIUC (Fall 2016 Lectures)
• undergraduate machine learning at UBC 2012, Nando de Freitas
• CS 229 - Machine Learning - Stanford University (Autumn 2018)
• CS 189/289A Introduction to Machine Learning, Prof Jonathan Shewchuk - UCBerkeley
• CPSC 340: Machine Learning and Data Mining (2018) - UBC
• CS4780/5780 Machine Learning, Fall 2013 - Cornell University
• CS4780/5780 Machine Learning, Fall 2018 - Cornell University (Youtube)
• CSE474/574 Introduction to Machine Learning - SUNY University at Buffalo
• CS 5350/6350 - Machine Learning, Fall 2016, University of Utah
• ECE 5984 Introduction to Machine Learning, Spring 2015 - Virginia Tech
• CSx824/ECEx242 Machine Learning, Bert Huang, Fall 2015 - Virginia Tech
• STA 4273H - Large Scale Machine Learning, Winter 2015 - University of Toronto
• CS 485/685 Machine Learning, Shai Ben-David, University of Waterloo
• STAT 441/841 Classification Winter 2017 , Waterloo
• 10-605 - Machine Learning with Large Datasets, Fall 2016 - CMU
• Information Theory, Pattern Recognition, and Neural Networks - University of Cambridge
• Python and machine learning - Stanford Crowd Course Initiative
• MOOC - Machine Learning Part 1a - Udacity/Georgia Tech (Part 1b Part 2 Part 3)
• Machine Learning and Pattern Recognition 2015/16- University of Edinburgh
• Introductory Applied Machine Learning 2015/16- University of Edinburgh
• Pattern Recognition Class (2012)- Universität Heidelberg
• Introduction to Machine Learning and Pattern Recognition - CBCSL OSU
• Introduction to Machine Learning - IIT Kharagpur
• Introduction to Machine Learning - IIT Madras
• Pattern Recognition - IISC Bangalore
• Pattern Recognition and Application - IIT Kharagpur
• Pattern Recognition - IIT Madras
• Machine Learning Summer School 2013 - Max Planck Institute for Intelligent Systems Tübingen
• Machine Learning - Professor Kogan (Spring 2016) - Rutgers
• CS273a: Introduction to Machine Learning (YouTube)
• Machine Learning Crash Course 2015
• COM4509/COM6509 Machine Learning and Adaptive Intelligence 2015-16
• 10715 Advanced Introduction to Machine Learning
• Introduction to Machine Learning - Spring 2018 - ETH Zurich
• Machine Learning - Pedro Domingos- University of Washington
• Advanced Machine Learning - 2019 - ETH Zürich
• Machine Learning (COMP09012)
• Probabilistic Machine Learning 2020 - University of Tübingen
• Statistical Machine Learning 2020 - Ulrike von Luxburg - University of Tübingen
• COMS W4995 - Applied Machine Learning - Spring 2020 - Columbia University
• Machine Learning for Engineers 2022 (YouTube)

Data Mining

• CSEP 546, Data Mining - Pedro Domingos, Sp 2016 - University of Washington (YouTube)
• CS 5140/6140 - Data Mining, Spring 2016, University of Utah (Youtube)
• CS 5955/6955 - Data Mining, University of Utah (YouTube)
• Statistics 202 - Statistical Aspects of Data Mining, Summer 2007 - Google (YouTube)
• MOOC - Text Mining and Analytics by ChengXiang Zhai
• Information Retrieval SS 2014, iTunes - HPI
• MOOC - Data Mining with Weka
• CS 290 DataMining Lectures
• CS246 - Mining Massive Data Sets, Winter 2016, Stanford University (YouTube)
• Data Mining: Learning From Large Datasets - Fall 2017 - ETH Zurich
• Information Retrieval - Spring 2018 - ETH Zurich
• CAP6673 - Data Mining and Machine Learning - FAU(Video lectures)
• Data Warehousing and Data Mining Techniques - Technische Universität Braunschweig, Germany

Data Science

• Data 8: The Foundations of Data Science - UC Berkeley (Summer 17)
• CSE519 - Data Science Fall 2016 - Skiena, SBU
• CS 109 Data Science, Harvard University (YouTube)
• 6.0002 Introduction to Computational Thinking and Data Science - MIT OCW
• Data 100 - Summer 19- UC Berkeley
• Distributed Data Analytics (WT 2017/18) - HPI University of Potsdam
• Statistics 133 - Concepts in Computing with Data, Fall 2013 - UC Berkeley
• Data Profiling and Data Cleansing (WS 2014/15) - HPI University of Potsdam
• AM 207 - Stochastic Methods for Data Analysis, Inference and Optimization, Harvard University
• CS 229r - Algorithms for Big Data, Harvard University (Youtube)
• Algorithms for Big Data - IIT Madras
• Python Data Science with the TCLab (YouTube)
• Probabilistic Graphical Modeling
• MOOC - Probabilistic Graphical Models - Coursera
• CS 6190 - Probabilistic Modeling, Spring 2016, University of Utah
• 10-708 - Probabilistic Graphical Models, Carnegie Mellon University
• Probabilistic Graphical Models, Daphne Koller, Stanford University
• Probabilistic Models - UNIVERSITY OF HELSINKI
• Probabilistic Modelling and Reasoning 2015/16- University of Edinburgh
• Probabilistic Graphical Models, Spring 2018 - Notre Dame

Deep Learning

• NYU Deep Learning Spring 2020
• NYU Deep Learning Spring 2021
• 6.S191: Introduction to Deep Learning - MIT
• Deep Learning CMU
• Part 1: Practical Deep Learning for Coders, v3 - fast.ai
• Part 2: Deep Learning from the Foundations - fast.ai
• Deep learning at Oxford 2015 - Nando de Freitas
• Self-Driving Cars — Andreas Geiger, 2021/22 (YouTube)
• 6.S094: Deep Learning for Self-Driving Cars - MIT
• CS294-129 Designing, Visualizing and Understanding Deep Neural Networks (YouTube)
• CS230: Deep Learning - Autumn 2018 - Stanford University
• STAT-157 Deep Learning 2019 - UC Berkeley
• Full Stack DL Bootcamp 2019 - UC Berkeley
• Deep Learning, Stanford University
• MOOC - Neural Networks for Machine Learning, Geoffrey Hinton 2016 - Coursera
• Deep Unsupervised Learning -- Berkeley Spring 2020
• Stat 946 Deep Learning - University of Waterloo
• Neural networks class - Université de Sherbrooke (YouTube)
• CS294-158 Deep Unsupervised Learning SP19
• DLCV - Deep Learning for Computer Vision - UPC Barcelona
• DLAI - Deep Learning for Artificial Intelligence @ UPC Barcelona
• Neural Networks and Applications - IIT Kharagpur
• UVA DEEP LEARNING COURSE
• Nvidia Machine Learning Class
• Deep Learning - Winter 2020-21 - Tübingen Machine Learning

Reinforcement Learning

• CS234: Reinforcement Learning - Winter 2019 - Stanford University
• Introduction to reinforcement learning - UCL
• Advanced Deep Learning & Reinforcement Learning - UCL
• Reinforcement Learning - IIT Madras
• CS885 Reinforcement Learning - Spring 2018 - University of Waterloo
• CS 285 - Deep Reinforcement Learning- UC Berkeley
• CS 294 112 - Reinforcement Learning
• NUS CS 6101 - Deep Reinforcement Learning
• ECE 8851: Reinforcement Learning
• CS294-112, Deep Reinforcement Learning Sp17 (YouTube)
• UCL Course 2015 on Reinforcement Learning by David Silver from DeepMind (YouTube)
• Deep RL Bootcamp - Berkeley Aug 2017
• Reinforcement Learning - IIT Madras

Advanced Machine Learning

• Machine Learning 2013 - Nando de Freitas, UBC
• Machine Learning, 2014-2015, University of Oxford
• 10-702/36-702 - Statistical Machine Learning - Larry Wasserman, Spring 2016, CMU (Spring 2015)
• 10-715 Advanced Introduction to Machine Learning - CMU (YouTube)
• CS 281B - Scalable Machine Learning, Alex Smola, UC Berkeley
• 18.409 Algorithmic Aspects of Machine Learning Spring 2015 - MIT
• CS 330 - Deep Multi-Task and Meta Learning - Fall 2019 - Stanford University (Youtube)

ML based Natural Language Processing and Computer Vision

• CS 224N - Deep Learning for Natural Language Processing, Stanford University (Lectures - Youtube)
• CS 224N - Natural Language Processing, Stanford University (Lecture videos)
• CS 124 - From Languages to Information - Stanford University
• MOOC - Natural Language Processing, Dan Jurafsky & Chris Manning - Coursera
• fast.ai Code-First Intro to Natural Language Processing (Github)
• MOOC - Natural Language Processing - Coursera, University of Michigan
• CS 231n - Convolutional Neural Networks for Visual Recognition, Stanford University
• CS224U: Natural Language Understanding - Spring 2019 - Stanford University
• Deep Learning for Natural Language Processing, 2017 - Oxford University
• Machine Learning for Robotics and Computer Vision, WS 2013/2014 - TU München (YouTube)
• Informatics 1 - Cognitive Science 2015/16- University of Edinburgh
• Informatics 2A - Processing Formal and Natural Languages 2016-17 - University of Edinburgh
• Computational Cognitive Science 2015/16- University of Edinburgh
• Accelerated Natural Language Processing 2015/16- University of Edinburgh
• Natural Language Processing - IIT Bombay
• NOC:Deep Learning For Visual Computing - IIT Kharagpur
• CS 11-747 - Neural Nets for NLP - 2019 - CMU
• Natural Language Processing - Michael Collins - Columbia University
• Deep Learning for Computer Vision - University of Michigan
• CMU CS11-737 - Multilingual Natural Language Processing

Time Series Analysis

• 02417 Time Series Analysis
• Applied Time Series Analysis

Misc Machine Learning Topics

• EE364a: Convex Optimization I - Stanford University
• CS 6955 - Clustering, Spring 2015, University of Utah
• Info 290 - Analyzing Big Data with Twitter, UC Berkeley school of information (YouTube)
• 10-725 Convex Optimization, Spring 2015 - CMU
• 10-725 Convex Optimization: Fall 2016 - CMU
• CAM 383M - Statistical and Discrete Methods for Scientific Computing, University of Texas
• CS224W Machine Learning with Graphs | Spring 2021 | Stanford University
• 9.520 - Statistical Learning Theory and Applications, Fall 2015 - MIT
• Reinforcement Learning - UCL
• Regularization Methods for Machine Learning 2016 (YouTube)
• Statistical Inference in Big Data - University of Toronto
• 10-725 Optimization Fall 2012 - CMU
• 10-801 Advanced Optimization and Randomized Methods - CMU (YouTube)
• Reinforcement Learning 2015/16- University of Edinburgh
• Reinforcement Learning - IIT Madras
• Statistical Rethinking Winter 2015 - Richard McElreath
• Music Information Retrieval - University of Victoria, 2014
• PURDUE Machine Learning Summer School 2011
• Foundations of Machine Learning - Blmmoberg Edu
• Introduction to reinforcement learning - UCL
• Advanced Deep Learning & Reinforcement Learning - UCL
• Web Information Retrieval (Proff. L. Becchetti - A. Vitaletti)
• Big Data Systems (WT 2019/20) - Prof. Dr. Tilmann Rabl - HPI
• Distributed Data Analytics (WT 2017/18) - Dr. Thorsten Papenbrock - HPI