An Ultimate Compilation of AI Resources for Mathematics, Machine Learning and Deep Learning

Knowledge Not Shared is wasted. - Clan Jacobs

This collection is a compilation of Excellent ML and DL Tutorials created by the people below

• Andrej Karpathy blog
• Brandon Roher
• Andrew Trask
• Jay Alammar
• Sebastian Ruder
• Distill
• StatQuest with Josh Starmer
• sentdex
• Lex Fridman
• 3Blue1Brown
• Alexander Amini
• The Coding Train
• Christopher Olah

Communities to Follow

• AI Coimbatore Join here🔗⬇️
  • Telegram : For Daily Updates
  • Facebook : Coimbatore School of AI
• TensorFlow User Group Coimbatore
  • Meetup : TFUGCbe
  • Facebook : TFUGCbe

This Repo is Created and Maintained by

Navaneeth Malingan

Why Data Science and how to get started?

• 🖥️ HOW TO GET STARTED WITH MACHINE LEARNING!
• How to Build a Meaningful Career in Data Science
• My Self-Created Artificial Intelligence Masters Degree
• PyImageSearch
• 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python

Intro to ML

• Luis Serrano: A Friendly Introduction to Machine Learning
• StatQuest: A Gentle Introduction to Machine Learning
• Machine Learning For Everyone Summarize's Machine Learning algorithms and their applications in simple words with real-world examples.

Anyone can do Machine Learning

• Teachable Machine Train a computer to recognize your own images, sounds, & poses. A fast, easy way to create machine learning models for your sites, apps, and more – no expertise or coding required.

MOOCs

• Machine Learning by Andrew Ng, Stanford IMDB 10/10 LOL :P
• Datacamp : Data Engineer with Python
• Intro to Machine Learning Topics Covered Naive Bayes, SVM, Decision Trees, Regressions, Outliers, Clustering, Feature Scaling, Text Learning, Feature Selection, PCA, Validation, Evaluation Metrics
• Intro to TensorFlow for Deep Learning The Best Course for Learning TensorFlow
• End-to-End Machine Learning
• NVIDIA DEEP LEARNING INSTITUTE
• Introduction to Machine Learning for Coders!
• Practical Deep Learning for Coders, v3
• FastAI

Courses from Top Universities

Stanford University

• CS221 - Artificial Intelligence: Principles and Techniques by Percy Liang and Dorsa Sadigh
• CS229 - Machine Learning by Andrew Ng
• CS230 - Deep Learning by Andrew Ng
• CS231n - Convolutional Neural Networks for Visual Recognition by Fei-Fei Li and Andrej Karpathy
• CS224n - Natural Language Processing with Deep Learning by Christopher Manning
• CS234 - Reinforcement Learning by Emma Brunskill
• CS330 - Deep Multi-task and Meta Learning by Chelsea Finn
• CS25 - Transformers United

Carnegie Mellon University

• CS/LTI 11-711: Advanced NLP by Graham Neubig
• CS/LTI 11-747: Neural Networks for NLP by Graham Neubig
• CS/LTI 11-737: Multilingual NLP by Graham Neubig
• CS/LTI 11-777: Multimodal Machine Learning by Louis-Philippe Morency
• CS/LTI 11-785: Introduction to Deep Learning by Bhiksha Raj and Rita Singh
• CS/LTI Low Resource NLP Bootcamp 2020 by Graham Neubig

Massachusetts Institute of Technology

• 6.S191 - Introduction to Deep Learning by Alexander Amini and Ava Amini
• 6.S094 - Deep Learning by Lex Fridman
• 6.S192 - Deep Learning for Art, Aesthetics, and Creativity by Ali Jahanian

University College London

• COMP M050 Reinforcement Learning by David Silver

YouTube ML Playlists

• Machine Learning by StatQuest with Josh Starmer
• Intelligence and Learning by The Coding Train

Machine Learning Glossary

• This glossary defines general machine learning terms in a variety of domains, as well as terms specific to TensorFlow.

Machine Learning Fundamentals (These terms will be often used in the below algorithms)

• Bias and Variance
• Cross Validation
• Machine Learning Fundamentals: The Confusion Matrix
• Sensitivity and Specivicity
• ROC and AUC, Clearly Explained!
• StatQuest: R-squared explained
• Regularization Part 1: Ridge Regression
• Regularization Part 2: Lasso Regression
• Maximum Likelihood
• Covariance and Correlation Part 1: Covariance
• Statistics Fundamentals: The Mean, Variance and Standard Deviation
• Statistics Fundamentals: Population Parameters
• Glossary: Statistics
• Glossary: Machine Learning
• Looking at R-Squared

Math

• Mathematics for Machine Learning In this post I have compiled great e-resources (MOOC, YouTube Lectures, Books) for learning Mathematics for Machine Learning.
• Mathematics for Machine Learning - Book One great book for all things math for machine learning. (free eBook)
• I highly Recommend you to go through the following resources by 3Blue1Brown
  • Essence of Linear Algrbra▶️
  • Essene of Calculus▶️
  • Differential equations▶️
• Gilbert Strang: Linear Algebra vs Calculus▶️
• Basics of Integral Calculus in Tamil▶️
• New fast.ai course: Computational Linear Algebra
• Linear Algebra Book

Python

• Python Programming Tutorials by Socratica▶️
• Python Tutorial by w3schools📙
• Learning Python Programming📙

Numpy

• A Visual Intro to NumPy and Data Representation
• CS231n : Python Numpy Tutorial
• NumPy resources : part of the End-to-End Machine Learning library
• 100 numpy exercises (with solutions)
• 101 NumPy Exercises for Data Analysis (Python)
• Numpy Tutorial – Introduction to ndarray
• Sci-Py Lectures : NumPy: creating and manipulating numerical data
• Python NumPy Tutorial for Beginners▶️ Learn the basics of the NumPy library in this tutorial for beginners. It provides background information on how NumPy works and how it compares to Python's Built-in lists. This video goes through how to write code with NumPy. It starts with the basics of creating arrays and then gets into more advanced stuff. The video covers creating arrays, indexing, math, statistics, reshaping, and more.
• Python NumPy Tutorial – Learn NumPy Arrays With Examples
• Python Numpy Array Tutorial
• NumPy Tutorial: Data analysis with Python
• Deep Learning Prerequisites: The Numpy Stack in Python▶️

Pandas

• [A Gentle Visual Intro to Data Analysis in Python U