Machine Learning & Deep Learning Tutorials

• This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list.

• If you want to contribute to this list, please read Contributing Guidelines.

• Curated list of R tutorials for Data Science, NLP and Machine Learning.

• Curated list of Python tutorials for Data Science, NLP and Machine Learning.

Contents

• Introduction
• Interview Resources
• Artificial Intelligence
• Genetic Algorithms
• Statistics
• Useful Blogs
• Resources on Quora
• Resources on Kaggle
• Cheat Sheets
• Classification
• Linear Regression
• Logistic Regression
• Model Validation using Resampling
  • Cross Validation
  • Bootstraping
• Deep Learning
  • Frameworks
  • Feed Forward Networks
  • Recurrent Neural Nets, LSTM, GRU
  • Restricted Boltzmann Machine, DBNs
  • Autoencoders
  • Convolutional Neural Nets
  • Graph Representation Learning
• Natural Language Processing
  • Topic Modeling, LDA
  • Word2Vec
• Computer Vision
• Support Vector Machine
• Reinforcement Learning
• Decision Trees
• Random Forest / Bagging
• Boosting
• Ensembles
• Stacking Models
• VC Dimension
• Bayesian Machine Learning
• Semi Supervised Learning
• Optimizations
• Other Useful Tutorials

<a name="general" />

Introduction

• Machine Learning Course by Andrew Ng (Stanford University)

• AI/ML YouTube Courses

• Curated List of Machine Learning Resources

• In-depth introduction to machine learning in 15 hours of expert videos

• An Introduction to Statistical Learning

• List of Machine Learning University Courses

• Machine Learning for Software Engineers

• Dive into Machine Learning

• A curated list of awesome Machine Learning frameworks, libraries and software

• A curated list of awesome data visualization libraries and resources.

• An awesome Data Science repository to learn and apply for real world problems

• The Open Source Data Science Masters

• Machine Learning FAQs on Cross Validated

• Machine Learning algorithms that you should always have a strong understanding of

• Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables

• List of Machine Learning Concepts

• Slides on Several Machine Learning Topics

• MIT Machine Learning Lecture Slides

• Comparison Supervised Learning Algorithms

• Learning Data Science Fundamentals

• Machine Learning mistakes to avoid

• Statistical Machine Learning Course

• TheAnalyticsEdge edX Notes and Codes

• Have Fun With Machine Learning

• Twitter's Most Shared #machineLearning Content From The Past 7 Days

• Grokking Machine Learning

<a name="interview" />

Interview Resources

• 41 Essential Machine Learning Interview Questions (with answers)

• How can a computer science graduate student prepare himself for data scientist interviews?

• How do I learn Machine Learning?

• FAQs about Data Science Interviews

• What are the key skills of a data scientist?

• The Big List of DS/ML Interview Resources

<a name="ai" />

Artificial Intelligence

• Awesome Artificial Intelligence (GitHub Repo)

• UC Berkeley CS188 Intro to AI, Lecture Videos, 2

• Programming Community Curated Resources for learning Artificial Intelligence

• MIT 6.034 Artificial Intelligence Lecture Videos, Complete Course

• edX course | Klein & Abbeel

• Udacity Course | Norvig & Thrun

• TED talks on AI

<a name="ga" />

Genetic Algorithms

• Genetic Algorithms Wikipedia Page

• Simple Implementation of Genetic Algorithms in Python (Part 1), Part 2

• Genetic Algorithms vs Artificial Neural Networks

• Genetic Algorithms Explained in Plain English

• Genetic Programming

  • Genetic Programming in Python (GitHub)

  • Genetic Alogorithms vs Genetic Programming (Quora), StackOverflow

<a name="stat" />

Statistics

• Stat Trek Website - A dedicated website to teach yourselves Statistics

• Learn Statistics Using Python - Learn Statistics using an application-centric programming approach

• Statistics for Hackers | Slides | @jakevdp - Slides by Jake VanderPlas

• Online Statistics Book - An Interactive Multimedia Course for Studying Statistics

• What is a Sampling Distribution?

• Tutorials

  • AP Statistics Tutorial

  • Statistics and Probability Tutorial

  • Matrix Algebra Tutorial

• What is an Unbiased Estimator?

• Goodness of Fit Explained

• What are QQ Plots?

• OpenIntro Statistics - Free PDF textbook

<a name="blogs" />

Useful Blogs

• Edwin Chen's Blog - A blog about Math, stats, ML, crowdsourcing, data science

• The Data School Blog - Data science for beginners!

• ML Wave - A blog for Learning Machine Learning

• Andrej Karpathy - A blog about Deep Learning and Data Science in general

• Colah's Blog - Awesome Neural Networks Blog

• Alex Minnaar's Blog - A blog about Machine Learning and Software Engineering

• Statistically Significant - Andrew Landgraf's Data Science Blog

• [Simply Statisti