Getting Started with Data Science a collection of learning resources

Data Science Learning Courses with Video Lectures

Artificial Intelligence

• CS 188 - Introduction to Artificial Intelligence, UC Berkeley
• 6.034 Artificial Intelligence, MIT OCW
• 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
• Artificial Intelligence: Knowledge Representation and Reasoning - IIT Madras
• MOOC - Intro to Artificial Intelligence - Udacity
• MOOC - Artificial Intelligence for Robotics - Udacity
• Graduate Course in Artificial Intelligence, Autumn 2012 - University of Washington
• Advanced AI Techniques - WS 2005 - Universität Freiburg (WS 2004)
• 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

Machine Learning

• Introduction to Machine Learning
  • MOOC Machine Learning Andrew Ng - Coursera/Stanford (Notes)
  • 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), CMU (YouTube)
  • 10-701 - Introduction to Machine Learning (PhD) - Tom Mitchell, Spring 2011, CMU (Fall 2014) (Spring 2015 by Alex Smola)
  • Microsoft Research - Machine Learning Course
  • CS 446 - Machine Learning, Fall 2016, UIUC(Fall 2015 Lectures)
  • undergraduate machine learning at UBC 2012, Nando de Freitas
  • CS 229 - Machine Learning - Stanford University
  • CS 189/289A Introduction to Machine Learning, Prof Jonathan Shewchuk - UCBerkeley
  • CS4780/5780 Machine Learning, Fall 2013 - Cornell University
  • 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 Data Mining - WS 2004 - Universität Freiburg
  • 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 - 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
• 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)
  • CAP6673 - Data Mining and Machine Learning - FAU
  • Data Warehousing and Data Mining Techniques - Technische Universität Braunschweig, Germany
• Data Science
  • Data 8: The Foundations of Data Science - UC Berkeley
  • 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
  • Statistics 133 - Concepts in Computing with Data, Fall 2013 - UC Berkeley
  • Data Profiling and Data Cleansing (WS 2014/15) - HPI
  • 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
• 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
• Deep Learning
  • 6.S191: Introduction to Deep Learning - MIT
  • Deep learning at Oxford 2015 - Nando de Freitas
  • 6.S094: Deep Learning for Self-Driving Cars - MIT
  • CS294-129 Designing, Visualizing and Understanding Deep Neural Networks (YouTube)
  • CS294-112, Deep Reinforcement Learning Sp17 (YouTube)
  • UCL Course 2015 on Reinforcement Learning by David Silver from DeepMind (YouTube)
  • Deep Learning, Stanford University
  • MOOC - Neural Networks for Machine Learning, Geoffrey Hinton 2016 - Coursera
  • Stat 946 Deep Learning - University of Waterloo
  • Neural networks class - Université de Sherbrooke (YouTube)
  • Neural Networks and Applications - IIT Kharagpur
  • Practical Deep Learning For Coders
  • UVA DEEP LEARNING COURSE
  • Nvidia Machine Learning Class
• 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
• ML based Natural Language Processing and Computer Vision
  • CS 224d - Deep Learning for Natural Language Processing, Stanford University (Lectures - Youtube)
  • CS 224N - Natural Language Processing, Stanford University
  • Natural Language Processing with Deep Learning, Winter 2017, Stanford University
  • MOOC - Natural Language Processing, Dan Jurafsky & Chris Manning - Coursera
  • MOOC - Natural Language Processing - Coursera, University of Michigan
  • CS 231n - Convolutional Neural Networks for Visual Recognition, 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
• Misc Machine Learning Topics
  • 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
  • CAM 383M - Statistical and Discrete Methods for Scientific Computing, University of Texas
  • 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