Machine Learning Learning Path - spinningideas/resources GitHub Wiki

AI Learning

• https://github.com/MarcosSete/awesome-free-ai-course-notes - A curated collection of machine learning and AI lecture notes from the world's leading universities. This repository gives you access to the same lecture notes used by students at top institutions such as MIT, helping you learn from the very best educational resources available.

Machine Learning - Learning Path

1. Youtube: 3b1b videos on Neural Networks, Calculus and Linear Algebra
2. Coursera: Andrew Ng's Machine Learning course
3. Book: Deep Learning with Python by Francois Chollet
4. Book: Reinforcement Learning: An Introduction, by Richard S. Sutton and Andrew G. Barto
5. Free Course

See also: https://github.com/louisfb01/start-machine-learning-in-2020

Core Concepts

• https://github.com/spinningideas/resources/wiki/Machine-Learning-101
• Statistics

Use Cases

• https://github.com/spinningideas/resources/wiki/Machine-Learning-Use-Cases

Python Libraries

Python libraries to be familiar with:

Machine Learning (ML)

• Sckit-learn
• xgboost
• catboost
• lightgbm
• hyperopt

Deep Learning (DL)

• Tensorflow
• PyTorch
• Keras

NLP and Transformers

• HuggingFace: https://huggingface.co/

Reinforcement Learning (RL)

• OpenAI Gym

Production

• MLFlow: https://mlflow.org/
• Apache Airflow: https://airflow.apache.org/
• Kubeflow: https://www.kubeflow.org/

Video Courses

Video Lectures for Machine Learning

Machine Learning

• Cornell CS4780: https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS

• Stanford CS 229: https://www.youtube.com/playlist?list=PLoROMvodv4rNH7qL6-efu_q2_bPuy0adh

• IIT Madras: https://www.youtube.com/playlist?list=PL1xHD4vteKYVpaIiy295pg6_SY5qznc77

• IISc Bangalore(Rigorous Math): https://www.youtube.com/playlist?list=PLbMVogVj5nJSlpmy0ni_5-RgbseafOViy

• Applied Machine Learning Cornell CS5787: https://www.youtube.com/playlist?list=PL2UML_KCiC0UlY7iCQDSiGDMovaupqc83

• Caltech's Machine Learning Course (CS 156 by Professor Yaser Abu-Mostafa): https://www.youtube.com/playlist?list=PL41qI9AD63BMXtmes0upOcPA5psKqVkgS

• StatQuest (Best resource for revision and visualization): https://www.youtube.com/user/joshstarmer?app=desktop

Deep Learning

IIT Madras (No prerequisites and great prof):

Part 1: https://youtube.com/playlist?list=PLyqSpQzTE6M9gCgajvQbc68Hk_JKGBAYT

Part 2: https://www.youtube.com/playlist?list=PLyqSpQzTE6M-_1jAqrFCsgCcuTYm_2urp

Course link for slides and references: http://www.cse.iitm.ac.in/~miteshk/CS7015_2018.html

• Neural Networks by Hinton: https://www.youtube.com/playlist?list=PLiPvV5TNogxKKwvKb1RKwkq2hm7ZvpHz0

• NYU DL (Taught by Prof Alfredo Canziani and Prof Yann Lecun): https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI

Computer Vision(Deep Learning)

• Michigan University: https://youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r

(This Michigan university course is the updated version of Stanford’s CS231n CV course and includes all the content covered by that as well)

• Advanced Deep Learning for Computer Vision by TU Munich: https://www.youtube.com/playlist?list=PLog3nOPCjKBnjhuHMIXu4ISE4Z4f2jm39

Natural Language Processing (Deep Learning)

• Stanford CS 224n: https://youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z

• Natural Language Understanding Stanford CS 224u: https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20

• Deep Learning for NLP at Oxford with Deep Mind 2017: https://www.youtube.com/playlist?list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm

• NLP CMU 11-411/11-611: https://www.youtube.com/playlist?list=PL4YhK0pT0ZhXteJ2OTzg4vgySjxTU_QUs

• CMU CS11-737 Multilingual Natural Language Processing: https://www.youtube.com/playlist?list=PL8PYTP1V4I8CHhppU6n1Q9-04m96D9gt5

Reinforcement Learning

• IIT Madras: https://youtube.com/playlist?list=PLEAYkSg4uSQ0Hkv_1LHlJtC_wqwVu6RQX

• Stanford CS234: https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u

Deep Reinforcement Learning

• UC Berkeley CS 285: https://youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc

• CS224W: Machine Learning with Graphs: https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn

• Stanford CS330: Multi-Task and Meta-Learning: https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5

• Explainable AI: https://www.youtube.com/playlist?list=PLV8yxwGOxvvovp-j6ztxhF3QcKXT6vORU

• Explainable AI in Industry: https://www.youtube.com/playlist?list=PL9ekywqME2Aj8OmKoBUaYEH7Xzi-YCRBy

Math Lectures

• Linear algebra(MIT): https://www.youtube.com/playlist?list=PLE7DDD91010BC51F8

• Optimization (IIT Kanpur): https://www.youtube.com/playlist?list=PLbMVogVj5nJRRbofh3Qm3P6_NVyevDGD_

• Multivariable Calculus(MIT): https://www.youtube.com/playlist?list=PL4C4C8A7D06566F38

• Probability and Statistics(Harvard): https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo

Education and Learning Resources

• https://atcold.github.io/pytorch-Deep-Learning/
• dive into deep learning
• https://www.deeplearningbook.org/
• Andrew Ng's courses
• http://neuralnetworksanddeeplearning.com
• aladdin persson
• abhishek thakur
• venelin valkov
• https://www.fast.ai/
• https://github.com/dreddnafious/thereisnospoon
• https://github.com/abhishek-ch/around-dataengineering
• https://github.com/snird/awesome-data-engineering-learning
• https://github.com/leehanchung/awesome-full-stack-machine-learning-courses

Machine Learning - Books

• https://github.com/ahkarami/Great-Deep-Learning-Books
• https://www.oreilly.com/library/view/programming-pytorch-for/9781492045342/
• https://github.com/changwookjun/StudyBook

Machine Learning - Papers

• https://github.com/minhlong94/must-read-ai-papers

Machine Learning - Get Started

Google Colab

• https://neptune.ai/blog/how-to-use-google-colab-for-deep-learning-complete-tutorial

If you want to explore deep learning and need a platform to help you do it - this tutorial is exactly for you.

In this tutorial you will learn:

• Getting around in Google Colab
• Installing python libraries in Colab
• Downloading large datasets in Colab
• Training a Deep learning model in Colab
• Using TensorBoard in Colab