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This list is a copy of ujjwalkarn/MachineLearningTutorials with ranks
Machine Learning & Deep Learning Tutorials ★87749

This repository contains a topicwise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list ★87749.

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

Curated list of R tutorials for Data Science, NLP and Machine Learning ★1075.

Curated list of Python tutorials for Data Science, NLP and Machine Learning ★3107.
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

Indepth introduction to machine learning in 15 hours of expert videos

A curated list of awesome Machine Learning frameworks, libraries and software ★33352

A curated list of awesome data visualization libraries and resources. ★1665

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

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

Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables

Twitter’s Most Shared #machineLearning Content From The Past 7 Days
Interview Resources

41 Essential Machine Learning Interview Questions (with answers)

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

MIT 6.034 Artificial Intelligence Lecture Videos, Complete Course

[edX course Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) 
[Udacity Course Norvig & Thrun](https://www.udacity.com/course/introtoartificialintelligence–cs271)  TED talks on AI
Genetic Algorithms
Statistics

Stat Trek Website  A dedicated website to teach yourselves Statistics

Learn Statistics Using Python ★524  Learn Statistics using an applicationcentric programming approach

[Statistics for Hackers Slides @jakevdp](https://speakerdeck.com/jakevdp/statisticsforhackers)  Slides by Jake VanderPlas 
Online Statistics Book  An Interactive Multimedia Course for Studying Statistics

Tutorials
 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](http://blog.kaggle.com/)  The Kaggle Blog about all things Data Science 
[A Quantitative Journey outlace](http://outlace.com/)  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 ★1391  Continuously updated machine learning documentations (mainly in Python3). Contents include educational implementation of machine learning algorithms from scratch and opensource library usage
Resources on Quora
Kaggle Competitions WriteUp
Cheat Sheets
Classification
Linear Regression

Multicollinearity and VIF

[Dummy Variable Trap Multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity)  Dealing with multicollinearity using VIFs

Logistic Regression

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

Pseudo R2 for Logistic Regression, How to calculate, Other Details
Model Validation using Resampling
 Cross Validation
 How to use crossvalidation in predictive modeling

Overfitting and Cross Validation

[Preventing Overfitting the Cross Validation Data Andrew Ng](http://ai.stanford.edu/~ang/papers/cvfinal.pdf) 
Overfitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
 How does CV overcome the Overfitting Problem

Deep Learning

A curated list of awesome Deep Learning tutorials, projects and communities ★9190

Interesting Deep Learning and NLP Projects (Stanford), Website

Understanding Natural Language with Deep Neural Networks Using Torch

Introduction to Deep Learning Using Python (GitHub) ★121 ⏳2Y, Good Introduction Slides

Video Lectures Oxford 2015, Video Lectures Summer School Montreal

Neural Machine Translation

Deep Learning Frameworks

Feed Forward Networks

Speeding up your Neural Network with Theano and the gpu, Code ★58 ⏳2Y

[ANN implemented in C++ AI Junkie](http://www.aijunkie.com/ann/evolved/nnt6.html)  Another Intro
 Recurrent and LSTM Networks

Recurrent Neural Net Tutorial Part 1, Part 2, Part 3, Code ★706

The Unreasonable effectiveness of RNNs, Torch Code ★8031, Python Code

Long Short Term Memory (LSTM)

Torch Code for characterlevel language models using LSTM ★8031

LSTM for Kaggle EEG Detection competition (Torch Code) ★64 ⏳2Y

[Deep Learning for Visual Q&A LSTM CNN](http://avisingh599.github.io/deeplearning/visualqa/), Code ★421 
[Computer Responds to email using LSTM Google](http://googleresearch.blogspot.in/2015/11/computerrespondtothisemail.html) 
LSTM dramatically improves Google Voice Search, Another Article

Torch code for Visual Question Answering using a CNN+LSTM model ★472 ⏳2Y
 LSTM for Human Activity Recognition ★1418 ⏳1Y

Gated Recurrent Units (GRU)

Time series forecasting with SequencetoSequence (seq2seq) rnn models ★385

Restricted Boltzmann Machine

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

Convolutional Neural Networks

[Interview with Yann LeCun Kaggle](http://blog.kaggle.com/2014/12/22/convolutionalnetsandcifar10aninterviewwithyanlecun/)  Visualising and Understanding CNNs
Natural Language Processing

A curated list of speech and natural language processing resources ★1746

Understanding Natural Language with Deep Neural Networks Using Torch

[NLP from Scratch Google Paper](https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf)

word2vec

Text Clustering

Text Classification

Kaggle Tutorial Bag of Words and Word vectors, Part 2, Part 3
Computer Vision
Support Vector Machine

[How does SVM Work Comparisons](http://stats.stackexchange.com/questions/23391/howdoesasupportvectormachinesvmwork) 
Comparisons

Software
 Kernels

Probabilities post SVM
Reinforcement Learning
Decision Trees

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

How do decision tree learning algorithms deal with missing values?

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

CTREE

CHAID

MARS

Probabilistic Decision Trees
Random Forest / Bagging

[OOB Estimate Explained RF vs LDA](https://stat.ethz.ch/education/semesters/ss2012/ams/slides/v10.2.pdf) 
Evaluating Random Forests for Survival Analysis Using Prediction Error Curve

Why doesn’t Random Forest handle missing values in predictors?
 Some Questions for R implementation, 2, 3
Boosting

[Introduction to Boosted Trees Tianqi Chen](https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf) 
Gradient Boosting Machine

xgboost

AdaBoost
Ensembles

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

[Good Resources Kaggle Africa Soil Property Prediction](https://www.kaggle.com/c/afsissoilproperties/forums/t/10391/bestensemblereferences)  How are classifications merged in an ensemble classifier?
Stacking Models
Vapnik–Chervonenkis Dimension
Bayesian Machine Learning
Semi Supervised Learning
Optimization

Mean Variance Portfolio Optimization with R and Quadratic Programming

Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters
Other Tutorials

For a collection of Data Science Tutorials using R, please refer to this list ★1075.

For a collection of Data Science Tutorials using Python, please refer to this list ★3107.
This list is a copy of ujjwalkarn/MachineLearningTutorials with ranks