Machine Learning & Deep Learning Tutorials 
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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.
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If you want to contribute to this list, please read Contributing Guidelines.
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Curated list of R tutorials for Data Science, NLP and Machine Learning.
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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
- Deep Learning
- Natural Language Processing
- 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
Introduction
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In-depth introduction to machine learning in 15 hours of expert videos
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A curated list of awesome Machine Learning frameworks, libraries and software
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A curated list of awesome data visualization libraries and resources.
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An awesome Data Science repository to learn and apply for real world problems
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Machine Learning algorithms that you should always have a strong understanding of
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Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables
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Twitter's Most Shared #machineLearning Content From The Past 7 Days
Interview Resources
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41 Essential Machine Learning Interview Questions (with answers)
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How can a computer science graduate student prepare himself for data scientist interviews?
Artificial Intelligence
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Programming Community Curated Resources for learning Artificial Intelligence
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MIT 6.034 Artificial Intelligence Lecture Videos, Complete Course
Genetic Algorithms
Statistics
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Stat Trek Website - A dedicated website to teach yourselves Statistics
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Learn Statistics Using Python - Learn Statistics using an application-centric programming approach
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Statistics for Hackers, Slides, @jakevdp - Slides by Jake VanderPlas
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Online Statistics Book - An Interactive Multimedia Course for Studying Statistics
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Tutorials
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OpenIntro Statistics - Free PDF textbook
Useful Blogs
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Edwin Chen's Blog - A blog about Math, stats, ML, crowdsourcing, data science
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The Data School Blog - Data science for beginners!
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ML Wave - A blog for Learning Machine Learning
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Andrej Karpathy - A blog about Deep Learning and Data Science in general
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Colah's Blog - Awesome Neural Networks Blog
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Alex Minnaar's Blog - A blog about Machine Learning and Software Engineering
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Statistically Significant - Andrew Landgraf's Data Science Blog
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Simply Statistics - A blog by three biostatistics professors
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Yanir Seroussi's Blog - A blog about Data Science and beyond
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fastML - Machine learning made easy
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Trevor Stephens Blog - Trevor Stephens Personal Page
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no free hunch, kaggle - The Kaggle Blog about all things Data Science
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A Quantitative Journey, outlace - learning quantitative applications
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r4stats - analyze the world of data science, and to help people learn to use R
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Variance Explained - David Robinson's Blog
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AI Junkie - a blog about Artificial Intellingence
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Deep Learning Blog by Tim Dettmers - Making deep learning accessible
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J Alammar's Blog- Blog posts about Machine Learning and Neural Nets
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Adam Geitgey - Easiest Introduction to machine learning
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Ethen's Notebook Collection - Continuously updated machine learning documentations (mainly in Python3). Contents include educational implementation of machine learning algorithms from scratch and open-source library usage
Resources on Quora
Kaggle Competitions WriteUp
Cheat Sheets
Classification
Linear Regression
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Multicollinearity and VIF
Logistic Regression
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Difference between logit and probit models, Logistic Regression Wiki, Probit Model Wiki
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Pseudo R2 for Logistic Regression, How to calculate, Other Details
Model Validation using Resampling
- Cross Validation
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Overfitting and Cross Validation
Deep Learning
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A curated list of awesome Deep Learning tutorials, projects and communities
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Interesting Deep Learning and NLP Projects (Stanford), Website
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Understanding Natural Language with Deep Neural Networks Using Torch
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Introduction to Deep Learning Using Python (GitHub), Good Introduction Slides
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Video Lectures Oxford 2015, Video Lectures Summer School Montreal
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Neural Machine Translation
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Deep Learning Frameworks
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Caffe
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TensorFlow
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Feed Forward Networks
- Recurrent and LSTM Networks
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The Unreasonable effectiveness of RNNs, Torch Code, Python Code
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Long Short Term Memory (LSTM)
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Gated Recurrent Units (GRU)
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Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models
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Restricted Boltzmann Machine
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Autoencoders: Unsupervised (applies BackProp after setting target = input)
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Convolutional Neural Networks
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Network Representation Learning
Natural Language Processing
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A curated list of speech and natural language processing resources
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Understanding Natural Language with Deep Neural Networks Using Torch
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word2vec
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Text Clustering
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Text Classification
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Named Entity Recognitation
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Kaggle Tutorial Bag of Words and Word vectors, Part 2, Part 3
Computer Vision
Support Vector Machine
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Comparisons
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Software
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Kernels
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Probabilities post SVM
Reinforcement Learning
Decision Trees
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What is entropy and information gain in the context of building decision trees?
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How do decision tree learning algorithms deal with missing values?
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Discover structure behind data with decision trees - Grow and plot a decision tree to automatically figure out hidden rules in your data
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Comparison of Different Algorithms
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CART
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CTREE
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CHAID
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MARS
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Probabilistic Decision Trees
Random Forest / Bagging
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Evaluating Random Forests for Survival Analysis Using Prediction Error Curve
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Why doesn't Random Forest handle missing values in predictors?
Boosting
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Gradient Boosting Machine
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xgboost
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AdaBoost
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CatBoost
Ensembles
Stacking Models
Vapnik–Chervonenkis Dimension
Bayesian Machine Learning
Semi Supervised Learning
Optimization
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Mean Variance Portfolio Optimization with R and Quadratic Programming
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Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters