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Learn Data Science - 06 Free Courses

2023-12-03

2294 字

11 分钟

Github

Programming

Artificial Intelligence

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
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
- 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
- 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

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