A-Z Machine Learning Resources
Published in
11 min readJan 9, 2020
Table Of Contents
- General Stuff
- 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
- Bootstrapping
- Deep Learning
- Frameworks
- Feed Forward Networks
- Recurrent Neural Nets, LSTM, GRU
- Restricted Boltzmann Machine, DBNs
- Autoencoders
- Convolution Neural Nets
- 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
General Stuff
- 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
- List of Machine Learning University Courses
- Machine Learning algorithms that you should always have a strong understanding of
- Differnce 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
Interview Resources
- 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?
Artificial Intelligence
- Awesome Artificial Intelligence (GitHub Repo)
- 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
- AP Statistics Tutorial
- Statistics and Probability Tutorial
- Matrix Algebra Tutorial
- What is an Unbiased Estimator?
- Goodness of Fit Explained
- What are QQ Plots?
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
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
Cheat Sheets
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
- An introduction to ROC analysis
- Simple guide to confusion matrix terminology
Linear Regression
- 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
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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
Model Validation using Resampling
- Resampling Explained
- Partioning data set in R
- Implementing hold-out Validaion in R, 2
- Cross Validation
- 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
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Deep Learning
- A curated list of awesome Deep Learning tutorials, projects and communities
- 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
- 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 Machine Translation
- 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
- 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
- Learning TensorFlow GitHub Repo
- Benchmark TensorFlow GitHub
- Feed Forward 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 Explained
- ANN implemented in C++ | AI Junkie
- Simple Implementation
- NN for Beginners
- Regression and Classification with NNs (Slides)
- Another Intro
- Recurrent and LSTM Networks
- 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
- LSTM
- Implementing LSTM from scratch, Python/Theano code
- Torch Code, Torch
- LSTM for Sentiment Analysis in Theano
- Deep Learning for Visual Q&A | LSTM | CNN, Code
- Computer Responds to email | Google
- LSTM dramatically improves Google Voice Search, 2
- Understanding Natural Language with Deep Neural Networks Using Torch
- Gated Recurrent Units (GRU)
- LSTM vs GRU
- 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
- Convolution 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
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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
- 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
- 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
- word2vec
- Google word2vec
- Bag of Words Model Wiki
- 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
- 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
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
- 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
Reinforcement Learning
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?
- 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
- 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
- AdaBoost
- AdaBoost Wiki, Python Code
- AdaBoost Sparse Input Support
- adaBag R package
- Tutorial
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?
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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
We are looking forward to the contributions on MLAIT. Click Here to Write For MLAIT or Register directly.
Thanks To Ujjwal Karna for sharing such an awesome list of Machine Learning Resources.
