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.
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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.

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

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

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

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

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

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
What is an Unbiased Estimator?
Goodness of Fit Explained
What are QQ Plots?
OpenIntro Statistics - Free PDF textbook

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 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

Resources on Quora

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

Kaggle Competitions WriteUp

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

Cheat Sheets

Probability Cheat Sheet, Source
Machine Learning Cheat Sheet
ML Compiled

Classification

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

Linear Regression

General
Multicollinearity and VIF
- Dummy Variable Trap | Multicollinearity
- Dealing with multicollinearity using VIFs
Residual Analysis
Outliers
- How should outliers be dealt with?
Elastic Net
- Regularization and Variable Selection via the Elastic Net

Logistic Regression

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

Model Validation using Resampling

Resampling Explained
Partioning data set in R
Implementing hold-out Validaion in R, 2

Cross Validation

Bootstrapping

Deep Learning

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

Feed Forward Networks

Recurrent and LSTM Networks

Recursive Neural Network (not Recurrent)
- Recursive Neural Tensor Network (RNTN)
- word2vec, DBN, RNTN for Sentiment Analysis

Restricted Boltzmann Machine

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

Convolutional Neural Networks

Network Representation Learning

Natural Language Processing

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

word2vec
Text Clustering
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

Computer Vision

Awesome computer vision (github)
Awesome deep vision (github)

Support Vector Machine

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
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

Reinforcement Learning

Awesome Reinforcement Learning (GitHub)
RL Tutorial Part 1, Part 2

Decision Trees

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

Random Forest / Bagging

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

Boosting for Better Predictions
Boosting Wikipedia Page
Introduction to Boosted Trees | Tianqi Chen
Gradient Boosting Machine
xgboost
AdaBoost
CatBoost

Ensembles

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 Models

Stacking, Blending and Stacked Generalization
Stacked Generalization (Stacking)
Stacked Generalization: when does it work?
Stacked Generalization Paper

Vapnik–Chervonenkis Dimension

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 Machine Learning

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

Semi Supervised Learning

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

Optimization

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

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