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
- Getting Started
- Mathematics
- Machine Learning
- Deep Learning
- Natural language processing
- Reinforcement learning
- Books
- ML in Production
- Quantum ML
- DataSets
- Other Useful Websites
- Other Useful GitRrpo
- Blogs and Webinar
- Must Read Research Paper
- Company Tech Blogs
| 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 |
- Papers with Code
- Two Minute Papers - Youtube
- The Missing Semester of Your CS Education
- Workera : Measure data-AI skills
- Machine learning mastery
- From Data to viz: Guide for your graph
- datatalks club
- Machine Learning for Art
- applyingml
- Deep Learning Drizzle
- The Machine & Deep Learning Compendium
- connectedpapers - Research Papers
- Papers and Latest Research - deepai
- Tracking Progress in NLP
- NLP Blogs by Sebastian Ruder
- labmlai for papers
- Applied-ml - Papers and blogs by organizations
- List Machine learning Python libraries
- ML From Scratch - Implementations of models/algorithms
- What the f*ck Python?
- scikit-learn user guide: step-step approach
- NLP Tutorial Code with DL
- awesome-mlops
- Text Classification Algorithms: A Survey
- ML use cases by company
NLP [Text]
- Text Classification Algorithms: A Survey
- Deep Learning Based Text Classification: A Comprehensive Review
- Compression of Deep Learning Models for Text: A Survey
- A Survey on Text Classification: From Shallow to Deep Learning
- A Survey of Transformers
- AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing
- Graph Neural Networks for Natural Language Processing: A Survey
- A Survey of Data Augmentation Approaches for NLP
- A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios
- Evaluation of Text Generation: A Survey
- A Survey of Transfer learning In NLP
- A Systematic Survey of Prompting Methods in NLP
OCR [Optical Character Recognition]