Free Aweasome Machine Learning(Basic & Advanced) Resources/Courses List
7 min readFeb 5, 2021
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
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
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
- 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
- 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 224d — 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
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
- 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
Probability & Statistics
- 6.041 Probabilistic Systems Analysis and Applied Probability — MIT OCW
- Statistics 110 — Probability — Harvard University
- STAT 2.1x: Descriptive Statistics | UC Berkeley
- STAT 2.2x: Probability | UC Berkeley
- MOOC — Statistics: Making Sense of Data, Coursera
- MOOC — Statistics One — Coursera
- Probability and Random Processes — IIT Kharagpur
- MOOC — Statistical Inference — Coursera
- 131B — Introduction to Probability and Statistics, UCI
- STATS 250 — Introduction to Statistics and Data Analysis, UMichigan
- Sets, Counting and Probability — Harvard
- Opinionated Lessons in Statistics (Youtube)
- Statistics — Brandon Foltz
- Statistical Rethinking: A Bayesian Course Using R and Stan (Lectures — Aalto University) (Book)
- 02402 Introduction to Statistics E12 — Technical University of Denmark (F17)
Linear Algebra
- 18.06 — Linear Algebra, Prof. Gilbert Strang, MIT OCW
- 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning — MIT OCW
- Linear Algebra (Princeton University)
- MOOC: Coding the Matrix: Linear Algebra through Computer Science Applications — Coursera
- CS 053 — Coding the Matrix — Brown University (Fall 14 videos)
- Linear Algebra Review — CMU
- A first course in Linear Algebra — N J Wildberger — UNSW
- INTRODUCTION TO MATRIX ALGEBRA
- Computational Linear Algebra — fast.ai (Github)
- 10–600 Math Background for ML — CMU
- MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
- 36–705 — Intermediate Statistics — Larry Wasserman, CMU (YouTube)
- Combinatorics — IISC Bangalore
- Advanced Engineering Mathematics — Notre Dame
- Statistical Computing for Scientists and Engineers — Notre Dame
- Statistical Computing, Fall 2017 — Notre Dame
- Mathematics for Machine Learning, Lectures by Ulrike von Luxburg — Tübingen Machine Learning
