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

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Machine Learning Course by Andrew Ng (Stanford University)

Renown entry-level online class with certificate. Taught by: Andrew Ng, Associate Professor, Stanford University; Chief Scientist, Baidu; Chairman and Co-founder, Coursera.

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

(by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani)

List of Machine Learning University Courses

by @prakhar1989 – University Computer Science courses across the web.

Machine Learning for Software Engineers

Dive into Machine Learning

"I learned Python by hacking first, and getting serious later. I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself."

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

The definitive curated list of machine learning frameworks, libraries and software organized by language. Covers Python, C++, Java, JavaScript, and more with comprehensive coverage of the ML ecosystem. CC0-1.0 licensed.

A curated list of awesome data visualization libraries and resources.

A curated list of awesome open-source data visualizations frameworks, libraries and software.

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

Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math.

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)

Curated list of artificial intelligence courses, books, video lectures, and papers for developers and researchers. MIT licensed.

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 Programming in Python.

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?

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

Concise machine learning cheat sheets covering key concepts and equations.

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

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?

Dummy Variable Trap | Multicollinearity

Dealing with multicollinearity using VIFs

Interpreting plot.lm() in R

How to interpret a QQ plot?

Interpreting Residuals vs Fitted Plot

How should outliers be dealt with?

Elastic Net

Regularization and Variable Selection via the Elastic Net

Logistic Regression Wiki

Easily plottable and understandable classification.

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

Cross Validation

Overfitting and Cross Validation

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

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

Learn how to build state of the art models without needing graduate-level math. 🆓

fast.ai - Cutting Edge Deep Learning For Coders

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

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

Deep Learning Papers Reading Roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

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

This book covers many of the core concepts behind neural networks and deep learning.

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

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

Scientific computing framework with wide support for machine learning algorithms, used by Facebook, Google, and more.

Torch ML Tutorial

, Code

Intro to Torch

Learning Torch GitHub Repo

Awesome-Torch (Repository on GitHub)

Tutorials, projects and communities for Torch, a scientific computing framework for LuaJIT.

Machine Learning using Torch Oxford Univ

, Code

Torch Internals Overview

Torch Cheatsheet

A scientific computing framework with wide support for machine learning algorithms that puts GPUs first. [BSD-3-Clause] website

Understanding Natural Language with Deep Neural Networks Using Torch

Deep Learning for Computer Vision with Caffe and cuDNN

Website

an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation…

TensorFlow Examples for Beginners

TensorFlow tutorials and code examples for beginners

Stanford Tensorflow for Deep Learning Research Course

GitHub Repo

available on Github.

Simplified Scikit-learn Style Interface to TensorFlow

TensorFlow wrapper à la scikit-learn.

Learning TensorFlow GitHub Repo

Benchmark TensorFlow GitHub

Awesome TensorFlow List

A list of all things related to TensorFlow.

TensorFlow Book

Android TensorFlow Machine Learning Example

Android TensorFlow Machine Learning Example.

GitHub Repo

Android TensorFlow MachineLearning Example (Building TensorFlow for Android)

Creating Custom Model For Android Using TensorFlow

GitHub Repo

A Quick Introduction to Neural Networks

Implementing a Neural Network from scratch

, 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)

RNNs code, theory and applications

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)

Understanding LSTM Networks

Explains the LSTM cells' inner workings, plus, it has interesting links in conclusion.

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)

Deep Learning for Visual Q&A | LSTM | CNN

, Code

Computer Responds to email using LSTM | Google

LSTM dramatically improves Google Voice Search

, Another Article

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

LSTM for Human Activity Recognition

Recurrent Neural Network classification in TensorFlow with LSTM on cellphone sensor data

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

Learn to use a seq2seq model on simple datasets as an introduction to the vast array of possibilities that this architecture offers

Recursive Neural Network (not Recurrent)

Recursive Neural Tensor Network (RNTN)

word2vec, DBN, RNTN for Sentiment Analysis

Beginner's Guide about RBMs

Introduction to RBMs

Hinton's Guide to Training RBMs

RBMs in R

Deep Belief Networks Tutorial

Andrew Ng Sparse Autoencoders pdf

Deep Autoencoders Tutorial

Denoising Autoencoders

, Theano Code

Stacked Denoising Autoencoders

An Intuitive Explanation of Convolutional Neural Networks

Awesome Deep Vision: List of Resources (GitHub)

Deep learning for computer vision

Intro to CNNs

Understanding CNN for NLP

Stanford Notes

, Codes, GitHub

JavaScript Library (Browser Based) for CNNs

ConvNetJS is a Javascript library for training Deep Learning models by Andrej Karpathy. GitHub

Using CNNs to detect facial keypoints

Deep learning to classify business photos at Yelp

, IFTTT, StackExchange, Raygun, Mozilla, Spotify, CERN, NASA Zalando

Interview with Yann LeCun | Kaggle

Visualising and Understanding CNNs

Awesome Graph Embedding

Curated list of articles related to deep learning scientific research on graph structured data at the graph level.

Awesome Network Embedding

Curated list of articles related to deep learning scientific research on graph structured data at the node level.

Network Representation Learning Papers

Knowledge Representation Learning Papers

Graph Based Deep Learning Literature

A curated list of speech and natural language processing resources

General List of NLP related resources (mostly not for Ruby programmers).

Understanding Natural Language with Deep Neural Networks Using Torch

tf-idf explained

Interesting Deep Learning and NLP Projects (Stanford)

, Website

The Stanford NLP Group

One of the top NLP research labs in the world, notable for creating Stanford CoreNLP and their coreference resolution system

NLP from Scratch | Google Paper

Graph Based Semi Supervised Learning for NLP

Bag of Words

Classification text with Bag of Words

Topic Modeling Wikipedia

Probabilistic Topic Models Princeton PDF

LDA Wikipedia

, LSA Wikipedia, Probabilistic LSA Wikipedia

What is a good explanation of Latent Dirichlet Allocation (LDA)?

Introduction to LDA

, Another good explanation

The LDA Buffet - Intuitive Explanation

Your Guide to Latent Dirichlet Allocation (LDA)

Difference between LSI and LDA

Original LDA Paper

alpha and beta in LDA

Intuitive explanation of the Dirichlet distribution

topicmodels: An R Package for Fitting Topic Models

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)

Deep Belief Nets for Topic Modeling

Gaussian LDA for Topic Models with Word Embeddings

Series of lecture notes for probabilistic topic models written in ipython notebook

Implementation of various topic models in Python

Google word2vec

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

How string clustering works

Levenshtein distance for measuring the difference between two sequences

Text clustering with Levenshtein distances

Stanford Named Entity Recognizer (NER)

Stanford NER is a Java implementation of a Named Entity Recognizer.

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)

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

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

LIBSVM

is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

Intro to SVM in R

What are Kernels in ML and SVM?

Intuition Behind Gaussian Kernel in SVMs?

Platt's Probabilistic Outputs for SVM

Platt Calibration Wiki

Why use Platts Scaling

Classifier Classification with Platt's Scaling

Awesome Reinforcement Learning (GitHub)

Reinforcement Learning.

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?

Discover structure behind data with decision trees

Grow and plot a decision tree to automatically figure out hidden rules in your data

CART vs CTREE

Comparison of complexity or performance

CHAID vs CART

, CART vs CHAID

Good Article on comparison

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

party package in R

Show volumne in each node using ctree in R

How to extract tree structure from ctree function?

Wikipedia Artice on CHAID

Basic Introduction to CHAID

Good Tutorial on CHAID

Wikipedia Article on MARS

Bayesian Learning in Probabilistic Decision Trees

Probabilistic Trees Research Paper

Awesome Random Forest (GitHub)**

Decision forest, tree-based methods, including random forest, bagging, and boosting.

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

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 tuning kaggle

xgboost vs gbm

xgboost survey

Practical XGBoost in Python online course (free)

AdaBoost Wiki

, Python Code

AdaBoost Sparse Input Support

adaBag R package

Tutorial

CatBoost Documentation

Benchmarks

is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Tutorial

GitHub Project

CatBoost vs. Light GBM vs. XGBoost

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)

by Cameron Davidson-Pilon. Introduction to Bayesian methods and probabilistic graphical models using tensorflow-probability (and, alternatively PyMC2/3).

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

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters, extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions. Licence: CC.

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

Learn to slay down hyperparameter spaces automatically rather than by hand.