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

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

• Cross Validation
• Bootstraping

• Frameworks
• Feed Forward Networks
• Recurrent Neural Nets, LSTM, GRU
• Restricted Boltzmann Machine, DBNs
• Autoencoders
• Convolutional Neural Nets

• Topic Modeling, LDA
• Word2Vec

Machine Learning Course by Andrew Ng (Stanford University)

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

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

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

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

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

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 Statistics - A blog by three biostatistics professors

Yanir Seroussi's Blog - A blog about Data Science and beyond

fastML - Machine learning made easy

Trevor Stephens Blog - Trevor Stephens Personal Page

no free hunch | kaggle - The Kaggle Blog about all things Data Science

A Quantitative Journey | outlace - learning quantitative applications

r4stats - analyze the world of data science, and to help people learn to use R

Variance Explained - David Robinson's Blog

AI Junkie - a blog about Artificial Intellingence

Deep Learning Blog by Tim Dettmers - Making deep learning accessible

J Alammar's Blog- Blog posts about Machine Learning and Neural Nets

Adam Geitgey - Easiest Introduction to machine learning

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

Most Viewed Machine Learning writers

Data Science Topic on Quora

William Chen's Answers

Michael Hochster's Answers

Ricardo Vladimiro's Answers

Storytelling with Statistics

Data Science FAQs on Quora

Machine Learning FAQs on Quora

How to almost win Kaggle Competitions

Convolution Neural Networks for EEG detection

Facebook Recruiting III Explained

Predicting CTR with Online ML

How to Rank 10% in Your First Kaggle Competition

Probability Cheat Sheet, Source

Machine Learning Cheat Sheet

ML Compiled

Does Balancing Classes Improve Classifier Performance?

What is Deviance?

When to choose which machine learning classifier?

What are the advantages of different classification algorithms?

ROC and AUC Explained (related video)

An introduction to ROC analysis

Simple guide to confusion matrix terminology

General

• Assumptions of Linear Regression, Stack Exchange

• Linear Regression Comprehensive Resource

• Applying and Interpreting Linear Regression

• What does having constant variance in a linear regression model mean?

• Difference between linear regression on y with x and x with y

• Is linear regression valid when the dependant variable is not normally distributed?

Multicollinearity and VIF

• Dummy Variable Trap | Multicollinearity

• Dealing with multicollinearity using VIFs

Residual Analysis

• Interpreting plot.lm() in R

• How to interpret a QQ plot?

• Interpreting Residuals vs Fitted Plot

Outliers

• How should outliers be dealt with?

Elastic Net

• Regularization and Variable Selection via the Elastic Net

Logistic Regression Wiki

Geometric Intuition of Logistic Regression

Obtaining predicted categories (choosing threshold)

Residuals in logistic regression

Difference between logit and probit models, Logistic Regression Wiki, Probit Model Wiki

Pseudo R2 for Logistic Regression, How to calculate, Other Details

Guide to an in-depth understanding of logistic regression

Resampling Explained

Partioning data set in R

Implementing hold-out Validaion in R, 2

• How to use cross-validation in predictive modeling

• Training with Full dataset after CV?

• Which CV method is best?

• Variance Estimates in k-fold CV

• Is CV a subsitute for Validation Set?

• Choice of k in k-fold CV

• CV for ensemble learning

• k-fold CV in R

• Good Resources

• Overfitting and Cross Validation

  • Preventing Overfitting the Cross Validation Data | Andrew Ng

  • Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

  • CV for detecting and preventing Overfitting

  • How does CV overcome the Overfitting Problem

Bootstrapping

• Why Bootstrapping Works?

• Good Animation

• Example of Bootstapping

• Understanding Bootstapping for Validation and Model Selection

• Cross Validation vs Bootstrap to estimate prediction error, Cross-validation vs .632 bootstrapping to evaluate classification performance

fast.ai - Practical Deep Learning For Coders

fast.ai - Cutting Edge Deep Learning For Coders

A curated list of awesome Deep Learning tutorials, projects and communities

Deep Learning Papers Reading Roadmap

Lots of Deep Learning Resources

Interesting Deep Learning and NLP Projects (Stanford), Website

Core Concepts of Deep Learning

Understanding Natural Language with Deep Neural Networks Using Torch

Stanford Deep Learning Tutorial

Deep Learning FAQs on Quora

Google+ Deep Learning Page

Recent Reddit AMAs related to Deep Learning, Another AMA

Where to Learn Deep Learning?

Deep Learning nvidia concepts

Introduction to Deep Learning Using Python (GitHub), Good Introduction Slides

Video Lectures Oxford 2015, Video Lectures Summer School Montreal

Deep Learning Software List

Hacker's guide to Neural Nets

Top arxiv Deep Learning Papers explained

Geoff Hinton Youtube Vidoes on Deep Learning

Awesome Deep Learning Reading List

Deep Learning Comprehensive Website, Software

deeplearning Tutorials

AWESOME! Deep Learning Tutorial

Deep Learning Basics

Intuition Behind Backpropagation

Stanford Tutorials

Train, Validation & Test in Artificial Neural Networks

Artificial Neural Networks Tutorials

Neural Networks FAQs on Stack Overflow

Deep Learning Tutorials on deeplearning.net

Neural Networks and Deep Learning Online Book

Neural Machine Translation

• Machine Translation Reading List

• Introduction to Neural Machine Translation with GPUs (part 1), Part 2, Part 3

• Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning

Deep Learning Frameworks

• Torch vs. Theano

• dl4j vs. torch7 vs. theano

• Deep Learning Libraries by Language

• Theano

  • Website

  • Theano Introduction

  • Theano Tutorial

  • Good Theano Tutorial

  • Logistic Regression using Theano for classifying digits

  • MLP using Theano

  • CNN using Theano

  • RNNs using Theano

  • LSTM for Sentiment Analysis in Theano

  • RBM using Theano

  • DBNs using Theano

  • All Codes

  • Deep Learning Implementation Tutorials - Keras and Lasagne

• Torch

  • Torch ML Tutorial, Code

  • Intro to Torch

  • Learning Torch GitHub Repo

  • Awesome-Torch (Repository on GitHub)

  • Machine Learning using Torch Oxford Univ, Code

  • Torch Internals Overview

  • Torch Cheatsheet

  • Understanding Natural Language with Deep Neural Networks Using Torch

• Caffe

  • Deep Learning for Computer Vision with Caffe and cuDNN

• TensorFlow

  • Website

  • TensorFlow Examples for Beginners

  • Stanford Tensorflow for Deep Learning Research Course

    • GitHub Repo

  • Simplified Scikit-learn Style Interface to TensorFlow

  • Learning TensorFlow GitHub Repo

  • Benchmark TensorFlow GitHub

  • Awesome TensorFlow List

  • TensorFlow Book

  • Android TensorFlow Machine Learning Example

    • GitHub Repo

  • Creating Custom Model For Android Using TensorFlow

    • GitHub Repo

Feed Forward Networks

• A Quick Introduction to Neural Networks

• Implementing a Neural Network from scratch, Code

• Speeding up your Neural Network with Theano and the gpu, Code

• Basic ANN Theory

• Role of Bias in Neural Networks

• Choosing number of hidden layers and nodes,2,3

• Backpropagation in Matrix Form

• ANN implemented in C++ | AI Junkie

• Simple Implementation

• NN for Beginners

• Regression and Classification with NNs (Slides)

• Another Intro

• awesome-rnn: list of resources (GitHub Repo)

• Recurrent Neural Net Tutorial Part 1, Part 2, Part 3, Code

• NLP RNN Representations

• The Unreasonable effectiveness of RNNs, Torch Code, Python Code

• Intro to RNN, LSTM

• An application of RNN

• Optimizing RNN Performance

• Simple RNN

• Auto-Generating Clickbait with RNN

• Sequence Learning using RNN (Slides)

• Machine Translation using RNN (Paper)

• Music generation using RNNs (Keras)

• Using RNN to create on-the-fly dialogue (Keras)

• Long Short Term Memory (LSTM)

  • Understanding LSTM Networks

  • LSTM explained

  • Beginner’s Guide to LSTM

  • Implementing LSTM from scratch, Python/Theano code

  • Torch Code for character-level language models using LSTM

  • LSTM for Kaggle EEG Detection competition (Torch Code)

  • LSTM for Sentiment Analysis in Theano

  • Deep Learning for Visual Q&A | LSTM | CNN, Code

  • Computer Responds to email using LSTM | Google

  • LSTM dramatically improves Google Voice Search, Another Article

  • Understanding Natural Language with LSTM Using Torch

  • Torch code for Visual Question Answering using a CNN+LSTM model

  • LSTM for Human Activity Recognition

• Gated Recurrent Units (GRU)

  • LSTM vs GRU

• Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models

Recursive Neural Network (not Recurrent)

• Recursive Neural Tensor Network (RNTN)

• word2vec, DBN, RNTN for Sentiment Analysis

Restricted Boltzmann Machine

• Beginner's Guide about RBMs

• Another Good Tutorial

• Introduction to RBMs

• Hinton's Guide to Training RBMs

• RBMs in R

• Deep Belief Networks Tutorial

• word2vec, DBN, RNTN for Sentiment Analysis

Autoencoders: Unsupervised (applies BackProp after setting target = input)

• Andrew Ng Sparse Autoencoders pdf

• Deep Autoencoders Tutorial

• Denoising Autoencoders, Theano Code

• Stacked Denoising Autoencoders

Convolutional Neural Networks

• An Intuitive Explanation of Convolutional Neural Networks

• Awesome Deep Vision: List of Resources (GitHub)

• Intro to CNNs

• Understanding CNN for NLP

• Stanford Notes, Codes, GitHub

• JavaScript Library (Browser Based) for CNNs

• Using CNNs to detect facial keypoints

• Deep learning to classify business photos at Yelp

• Interview with Yann LeCun | Kaggle

• Visualising and Understanding CNNs

A curated list of speech and natural language processing resources

Understanding Natural Language with Deep Neural Networks Using Torch

tf-idf explained

Interesting Deep Learning NLP Projects Stanford, Website

The Stanford NLP Group

NLP from Scratch | Google Paper

Graph Based Semi Supervised Learning for NLP

Bag of Words

• Classification text with Bag of Words

Topic Modeling

• LDA, LSA, Probabilistic LSA

• What is a good explanation of Latent Dirichlet Allocation?

• Awesome LDA Explanation!. Another good explanation

• The LDA Buffet- Intuitive Explanation

• Difference between LSI and LDA

• Original LDA Paper

• alpha and beta in LDA

• Intuitive explanation of the Dirichlet distribution

• Topic modeling made just simple enough

• Online LDA, Online LDA with Spark

• LDA in Scala, Part 2

• Segmentation of Twitter Timelines via Topic Modeling

• Topic Modeling of Twitter Followers

• Multilingual Latent Dirichlet Allocation (LDA). (Tutorial here)

word2vec

• Google word2vec

• Bag of Words Model Wiki

• word2vec Tutorial

• A closer look at Skip Gram Modeling

• Skip Gram Model Tutorial, CBoW Model

• Word Vectors Kaggle Tutorial Python, Part 2

• Making sense of word2vec

• word2vec explained on deeplearning4j

• Quora word2vec

• Other Quora Resources, 2, 3

• word2vec, DBN, RNTN for Sentiment Analysis

Text Clustering

• How string clustering works

• Levenshtein distance for measuring the difference between two sequences

• Text clustering with Levenshtein distances

Text Classification

• Classification Text with Bag of Words

Named Entity Recognitation

• Stanford Named Entity Recognizer (NER)

• Named Entity Recognition: Applications and Use Cases- Towards Data Science

Language learning with NLP and reinforcement learning

Kaggle Tutorial Bag of Words and Word vectors, Part 2, Part 3

What would Shakespeare say (NLP Tutorial)

A closer look at Skip Gram Modeling

Awesome computer vision (github)

Awesome deep vision (github)

Highest Voted Questions about SVMs on Cross Validated

Help me Understand SVMs!

SVM in Layman's terms

How does SVM Work | Comparisons

A tutorial on SVMs

Practical Guide to SVC, Slides

Introductory Overview of SVMs

Comparisons

• SVMs > ANNs, ANNs > SVMs, Another Comparison

• Trees > SVMs

• Kernel Logistic Regression vs SVM

• Logistic Regression vs SVM, 2, 3

Optimization Algorithms in Support Vector Machines

Variable Importance from SVM

Software

• LIBSVM

• Intro to SVM in R

Kernels

• What are Kernels in ML and SVM?

• Intuition Behind Gaussian Kernel in SVMs?

Probabilities post SVM

• Platt's Probabilistic Outputs for SVM

• Platt Calibration Wiki

• Why use Platts Scaling

• Classifier Classification with Platt's Scaling

Awesome Reinforcement Learning (GitHub)

RL Tutorial Part 1, Part 2

Wikipedia Page - Lots of Good Info

FAQs about Decision Trees

Brief Tour of Trees and Forests

Tree Based Models in R

How Decision Trees work?

Weak side of Decision Trees

Thorough Explanation and different algorithms

What is entropy and information gain in the context of building decision trees?

Slides Related to Decision Trees

How do decision tree learning algorithms deal with missing values?

Using Surrogates to Improve Datasets with Missing Values

Good Article

Are decision trees almost always binary trees?

Pruning Decision Trees, Grafting of Decision Trees

What is Deviance in context of Decision Trees?

Discover structure behind data with decision trees - Grow and plot a decision tree to automatically figure out hidden rules in your data

Comparison of Different Algorithms

• CART vs CTREE

• Comparison of complexity or performance

• CHAID vs CART , CART vs CHAID

• Good Article on comparison

CART

• Recursive Partitioning Wikipedia

• CART Explained

• How to measure/rank “variable importance” when using CART?

• Pruning a Tree in R

• Does rpart use multivariate splits by default?

• FAQs about Recursive Partitioning

CTREE

• party package in R

• Show volumne in each node using ctree in R

• How to extract tree structure from ctree function?

CHAID

• Wikipedia Artice on CHAID

• Basic Introduction to CHAID

• Good Tutorial on CHAID

MARS

• Wikipedia Article on MARS

Probabilistic Decision Trees

• Bayesian Learning in Probabilistic Decision Trees

• Probabilistic Trees Research Paper

Awesome Random Forest (GitHub)**

How to tune RF parameters in practice?

Measures of variable importance in random forests

Compare R-squared from two different Random Forest models

OOB Estimate Explained | RF vs LDA

Evaluating Random Forests for Survival Analysis Using Prediction Error Curve

Why doesn't Random Forest handle missing values in predictors?

How to build random forests in R with missing (NA) values?

FAQs about Random Forest, More FAQs

Obtaining knowledge from a random forest

Some Questions for R implementation, 2, 3

Boosting for Better Predictions

Boosting Wikipedia Page

Introduction to Boosted Trees | Tianqi Chen

Gradient Boosting Machine

• Gradiet Boosting Wiki

• Guidelines for GBM parameters in R, Strategy to set parameters

• Meaning of Interaction Depth, 2

• Role of n.minobsinnode parameter of GBM in R

• GBM in R

• FAQs about GBM

• GBM vs xgboost

xgboost

• xgboost tuning kaggle

• xgboost vs gbm

• xgboost survey

• Practical XGBoost in Python online course (free)

AdaBoost

• AdaBoost Wiki, Python Code

• AdaBoost Sparse Input Support

• adaBag R package

• Tutorial

Wikipedia Article on Ensemble Learning

Kaggle Ensembling Guide

The Power of Simple Ensembles

Ensemble Learning Intro

Ensemble Learning Paper

Ensembling models with R, Ensembling Regression Models in R, Intro to Ensembles in R

Ensembling Models with caret

Bagging vs Boosting vs Stacking

Good Resources | Kaggle Africa Soil Property Prediction

Boosting vs Bagging

Resources for learning how to implement ensemble methods

How are classifications merged in an ensemble classifier?

Stacking, Blending and Stacked Generalization

Stacked Generalization (Stacking)

Stacked Generalization: when does it work?

Stacked Generalization Paper

Wikipedia article on VC Dimension

Intuitive Explanantion of VC Dimension

Video explaining VC Dimension

Introduction to VC Dimension

FAQs about VC Dimension

Do ensemble techniques increase VC-dimension?

Bayesian Methods for Hackers (using pyMC)

Should all Machine Learning be Bayesian?

Tutorial on Bayesian Optimisation for Machine Learning

Bayesian Reasoning and Deep Learning, Slides

Bayesian Statistics Made Simple

Kalman & Bayesian Filters in Python

Markov Chain Wikipedia Page

Wikipedia article on Semi Supervised Learning

Tutorial on Semi Supervised Learning

Graph Based Semi Supervised Learning for NLP

Taxonomy

Video Tutorial Weka

Unsupervised, Supervised and Semi Supervised learning

Research Papers 1, 2, 3

Mean Variance Portfolio Optimization with R and Quadratic Programming

Algorithms for Sparse Optimization and Machine Learning

Optimization Algorithms in Machine Learning, Video Lecture

Optimization Algorithms for Data Analysis

Video Lectures on Optimization

Optimization Algorithms in Support Vector Machines

The Interplay of Optimization and Machine Learning Research

Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters

For a collection of Data Science Tutorials using R, please refer to this list.

For a collection of Data Science Tutorials using Python, please refer to this list.