A Free Machine Learning University
Machine Learning Open Source University is an IDEA of free-learning of a ML enthusiast for all other ML enthusiast
This list is continuously updated - And if you are a Ml practitioner and have some good suggestions to improve this or have somegood resources to share, you create pull request and contribute.
Table of Contents
Title and Source | Link |
---|---|
Elements of AI : Part-1 | WebSite |
Elements of AI : Part-2 | WebSite |
CS50’s Introduction to AI Harvard | Cs50 WebSite |
Intro to Computational Thinking and Data Science MIT | WebSite |
Practical Data Ethics | fast.ai |
Machine learning Mastery Getting Started | machinelearningmastery |
Design and Analysis of Algorithms MIT | ocw.mit.edu |
AI: Principles and Techniques Stanford | YouTube |
The Private AI Series | openmined |
Title and Source | Link |
---|---|
Statistics in Machine Learning (Krish Naik) | YouTube |
Computational Linear Algebra for Coders | fast.ai |
Linear Algebra MIT | WebSite |
Statistics by zstatistics | WebSite |
Essence of linear algebra by 3Blue1Brown | YouTube |
SEEING THEORY (Visual Probability) brown | WebSite |
Matrix Methods in Data Analysis,and Machine Learning MIT | WebSite |
Math for Machine Learning | YouTube |
Statistics for Applications MIT | YouTube |
Title and Source | Link |
---|---|
Introduction to Machine Learning with scikit-learn | dataschool |
Introduction to Machine Learning | sebastianraschka |
Open Machine Learning Course | mlcourse.ai |
Machine Learning (CS229) Stanford | WebSite YouTube |
Introduction to Machine Learning MIT | WebSite |
Machine Learning Systems Design 2021 (CS329S) Stanford | WebSite |
Applied Machine Learning 2020 (CS5787) Cornell Tech | YouTube |
Machine Learning for Healthcare MIT | WebSite |
Machine Learning for Trading Georgia Tech | WebSite |
Introduction to Machine Learning for Coders | fast.ai |
Machine Learning Crash Course | Google AI |
Machine Learning with Python | freecodecamp |
Deep Reinforcement Learning:CS285 UC Berkeley | YouTube |
Probabilistic Machine Learning University of Tübingen | YouTube |
Machine Learning with Graphs(CS224W) Stanford | YouTube |
Machine Learning in Production CMU | WebSite |
Machine Learning & Deep Learning Fundamentals | deeplizard |
Interpretability and Explainability in Machine Learning | WebSite |
Practical Machine Learning 2021 Stanford | WebSite |
Machine Learning VU University | WebSite |
Machine Learning for Cyber Security Purdue University | YouTube |
Audio Signal Processing for Machine Learning | YouTube |
Machine learning & causal inference Stanford | YouTube |
Machine learning cs156 caltech | YouTube |
Multimodal machine learning (MMML) CMU | WebSite YouTube |
Advanced Topics in Machine Learning Caltech | WebSite |
Title and Source | Link |
---|---|
Introduction to Deep Learning(6.S191) MIT | YouTube |
Introduction to Deep Learning | sebastianraschka |
Deep Learning NYU | WebSite 2021 |
Deep Learning (CS182) UC Berkeley | YouTube |
Deep Learning Lecture Series DeepMind x UCL | YouTube |
Deep Learning (CS230) Stanford | WebSite |
CNN for Visual Recognition(CS231n) Stanford | WebSite-2020 YouTube-2017 |
Full Stack Deep Learning | WebSite2021 |
Practical Deep Learning for Coders, v3 | fast.ai |
Deep Learning Crash Course 2021 d2l.ai | YouTube |
Deep Learning for Computer Vision Michigan | WebSite |
Neural Networks from Scratch in Python by Sentdex | YouTube |
Keras - Python Deep Learning Neural Network API | deeplizard |
Reproducible Deep Learning | sscardapane.it |
PyTorch Fundamentals | microsoft |
Geometric Deep Learing (GDL100) | geometricdeeplearning |
Deep learning Neuromatch Academy | neuromatch |
Deep Learning for Molecules and Materials | WebSite |
Deep Learning course for Vision | arthurdouillard.com |
Deep Multi-Task and Meta Learning (CS330) Stanford | WebSite YouTube |
Deep Learning Interviews book | WebSite |
Deep Learning for Computer Vision 2021 | YouTube |
Deep Learning 2022 CMU | YouTube |
UvA Deep Learning | WebSite |
Title and Source | Link |
---|---|
Natural Language Processing AWS | YouTube |
NLP - Krish Naik | YouTube |
NLP with Deep Learning(CS224N) 2019 Stanford | YouTube 2021 |
A Code-First Introduction to Natural Language Processing | fast.ai |
CMU Neural Nets for NLP 2021 Carnegie Mellon University | YouTube |
Speech and Language Processing Stanford | WebSite |
Natural Language Understanding (CS224U) Stanford | YouTube 2022 |
NLP with Dan Jurafsky and Chris Manning, 2012 Stanford | YouTube |
Intro to NLP with spaCy | YouTube |
Advanced NLP with spaCy | website |
Applied Language Technology | website |
Advanced Natural Language Processing Umass | website YouTube 2020 |
Huggingface Course | huggingface.co |
NLP Course Michigan | github |
Multilingual NLP 2020 CMU | YouTube |
Advanced NLP 2021 CMU | YouTube |
Transformers United stanford | Website YouTube |
CS324 Large Language Models | Website |
Title and Source | Link |
---|---|
Reinforcement Learning(CS234) Stanford | YouTube-2019 |
Introduction to reinforcement learning DeepMind | YouTube-2015 |
Reinforcement Learning Course DeepMind & UCL | YouTube-2018 |
Advanced Deep Learning & Reinforcement Learning | YouTube |
DeepMind x UCL Reinforcement Learning 2021 | YouTube |
Title and Source | Link |
---|---|
Scientific Python Lectures | ScipyLectures |
Mathematics for Machine Learning | mml-book |
An Introduction to Statistical Learning | statlearning |
Think Stats | Think Stats |
Python Data Science Handbook | Python For DS |
Natural Language Processing with Python - NLTK | NLTK |
Deep Learning by Ian Goodfellow | deeplearningbook |
Dive into Deep Learning | d2l.ai |
Approaching (Almost) Any Machine Learning Problem | AAANLP |
Neural networks and Deep learning | neuralnetworksanddeeplearning |
AutoML: Methods, Systems, Challenges (first book on AutoML) | automl |
Feature Engineering and Selection | bookdown.org |
Introduction to Machine Learning Interviews Book | huyenchip.com |
Hands-On Machine Learning with R | website |
Zero to Mastery TensorFlow for Deep Learning Book | dev.mrdbourke.com/ |
Introduction to Probability for Data Science | probability4datascience |
Graph Representation Learning Book | cs.mcgill.ca |
Interpretable Machine Learning | christophm |
Computer Vision: Algorithms and Applications, 2nd ed. | szeliski.org |
Title and Source | Link |
---|---|
Introduction to Docker | Docker |
MLOps Basics | GitHub |
Effective MLOps: Model Development | wandb |
Title and Source | Link |
---|---|
Quantum machine learning | pennylane.ai |
Title and Source | Link |
---|---|
Yelp Open Dataset | yelp |
Machine Translation | website |
IndicNLP Corpora (Indian languages) | ai4bharat |
Amazon product co-purchasing network metadata | snap.stanford.edu/ |
Stanford Question Answering Dataset (SQuAD) | website |
NLP [Text]
OCR [Optical Character Recognition]