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.
In-depth introduction to machine learning in 15 hours of expert videos
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
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
41 Essential Machine Learning Interview Questions (with answers)
How can a computer science graduate student prepare himself for data scientist interviews?
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
Tutorials
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
Multicollinearity and VIF
Difference between logit and probit models, Logistic Regression Wiki, Probit Model Wiki
Pseudo R2 for Logistic Regression, How to calculate, Other Details
Overfitting and Cross Validation
A curated list of awesome Deep Learning tutorials, projects and communities
Interesting Deep Learning and NLP Projects (Stanford), Website
Understanding Natural Language with Deep Neural Networks Using Torch
Introduction to Deep Learning Using Python (GitHub), Good Introduction Slides
Video Lectures Oxford 2015, Video Lectures Summer School Montreal
Neural Machine Translation
Deep Learning Frameworks
Caffe
TensorFlow
Feed Forward Networks
The Unreasonable effectiveness of RNNs, Torch Code, Python Code
Long Short Term Memory (LSTM)
Gated Recurrent Units (GRU)
Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models
Restricted Boltzmann Machine
Autoencoders: Unsupervised (applies BackProp after setting target = input)
Convolutional Neural Networks
A curated list of speech and natural language processing resources
Understanding Natural Language with Deep Neural Networks Using Torch
word2vec
Text Clustering
Text Classification
Kaggle Tutorial Bag of Words and Word vectors, Part 2, Part 3
Comparisons
Software
Kernels
Probabilities post SVM
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
Evaluating Random Forests for Survival Analysis Using Prediction Error Curve
Why doesn't Random Forest handle missing values in predictors?
Gradient Boosting Machine
xgboost
AdaBoost
Mean Variance Portfolio Optimization with R and Quadratic Programming
Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters