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

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

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

Python libraries to be familiar with:

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

• Tensorflow
• PyTorch
• Keras

• HuggingFace: https://huggingface.co/

• OpenAI Gym

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

Video Lectures for 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

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

• 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

• 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

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

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

• 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

• 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

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

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

• 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