How to get started with ML

Contents

• Mathematics
• 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

Math

Learn some math basics! Focus only on these topics, then come back later in case you need to learn more.

• Khan Academy - Multivariable Calculus (opens in a new tab)
• Khan Academy - Differential Equations (opens in a new tab)
• Khan Academy - Linear Algebra (opens in a new tab)
• Khan Academy - Statistics Probability (opens in a new tab)
• Optional: 3Blue1Brown - Essence of Linear Algebra (opens in a new tab)
• Dedicated section (opens in a new tab) If you really want to get deep into this.

Introduction

• Machine Learning Course by Andrew Ng (Stanford University) (opens in a new tab)

• AI/ML YouTube Courses (opens in a new tab)

• In-depth introduction to machine learning in 15 hours of expert videos (opens in a new tab)

• An Introduction to Statistical Learning (opens in a new tab)

• List of Machine Learning University Courses (opens in a new tab)

• Machine Learning for Software Engineers (opens in a new tab)

• Dive into Machine Learning (opens in a new tab)

• A curated list of awesome Machine Learning frameworks, libraries and software (opens in a new tab)

• A curated list of awesome data visualization libraries and resources. (opens in a new tab)

• An awesome Data Science repository to learn and apply for real world problems (opens in a new tab)

• The Open Source Data Science Masters (opens in a new tab)

• Machine Learning FAQs on Cross Validated (opens in a new tab)

• Machine Learning algorithms that you should always have a strong understanding of (opens in a new tab)

• Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables (opens in a new tab)

• List of Machine Learning Concepts (opens in a new tab)

• Slides on Several Machine Learning Topics (opens in a new tab)

• MIT Machine Learning Lecture Slides (opens in a new tab)

• Comparison Supervised Learning Algorithms (opens in a new tab)

• Learning Data Science Fundamentals (opens in a new tab)

• Machine Learning mistakes to avoid (opens in a new tab)

• Statistical Machine Learning Course (opens in a new tab)

• TheAnalyticsEdge edX Notes and Codes (opens in a new tab)

• Have Fun With Machine Learning (opens in a new tab)

• Twitter's Most Shared #machineLearning Content From The Past 7 Days (opens in a new tab)

• Grokking Machine Learning (opens in a new tab)

Interview Resources

• 41 Essential Machine Learning Interview Questions (with answers) (opens in a new tab)

• How can a computer science graduate student prepare himself for data scientist interviews? (opens in a new tab)

• How do I learn Machine Learning? (opens in a new tab)

• FAQs about Data Science Interviews (opens in a new tab)

• What are the key skills of a data scientist? (opens in a new tab)

• The Big List of DS/ML Interview Resources (opens in a new tab)

Artificial Intelligence

• Awesome Artificial Intelligence (GitHub Repo) (opens in a new tab)

• UC Berkeley CS188 Intro to AI (opens in a new tab), Lecture Videos (opens in a new tab), 2 (opens in a new tab)

• Programming Community Curated Resources for learning Artificial Intelligence (opens in a new tab)

• MIT 6.034 Artificial Intelligence Lecture Videos (opens in a new tab), Complete Course (opens in a new tab)

• edX course | Klein & Abbeel (opens in a new tab)

• Udacity Course | Norvig & Thrun (opens in a new tab)

• TED talks on AI (opens in a new tab)

Genetic Algorithms

• Genetic Algorithms Wikipedia Page (opens in a new tab)

• Simple Implementation of Genetic Algorithms in Python (Part 1) (opens in a new tab), Part 2 (opens in a new tab)

• Genetic Algorithms vs Artificial Neural Networks (opens in a new tab)

• Genetic Algorithms Explained in Plain English (opens in a new tab)

• Genetic Programming (opens in a new tab)

  • Genetic Programming in Python (GitHub) (opens in a new tab)

  • Genetic Alogorithms vs Genetic Programming (Quora) (opens in a new tab), StackOverflow (opens in a new tab)

Statistics

• Stat Trek Website (opens in a new tab) - A dedicated website to teach yourselves Statistics

• Learn Statistics Using Python (opens in a new tab) - Learn Statistics using an application-centric programming approach

• Statistics for Hackers | Slides | @jakevdp (opens in a new tab) - Slides by Jake VanderPlas

• Online Statistics Book (opens in a new tab) - An Interactive Multimedia Course for Studying Statistics

• What is a Sampling Distribution? (opens in a new tab)

• Tutorials

  • AP Statistics Tutorial (opens in a new tab)

  • Statistics and Probability Tutorial (opens in a new tab)

  • Matrix Algebra Tutorial (opens in a new tab)

• What is an Unbiased Estimator? (opens in a new tab)

• Goodness of Fit Explained (opens in a new tab)

• What are QQ Plots? (opens in a new tab)

• OpenIntro Statistics (opens in a new tab) - Free PDF textbook

Useful Blogs

• Edwin Chen's Blog (opens in a new tab) - A blog about Math, stats, ML, crowdsourcing, data science

• The Data School Blog (opens in a new tab) - Data science for beginners!

• ML Wave (opens in a new tab) - A blog for Learning Machine Learning

• Andrej Karpathy (opens in a new tab) - A blog about Deep Learning and Data Science in general

• Colah's Blog (opens in a new tab) - Awesome Neural Networks Blog

• Alex Minnaar's Blog (opens in a new tab) - A blog about Machine Learning and Software Engineering

• Statistically Significant (opens in a new tab) - Andrew Landgraf's Data Science Blog

• Simply Statistics (opens in a new tab) - A blog by three biostatistics professors

• Yanir Seroussi's Blog (opens in a new tab) - A blog about Data Science and beyond

• fastML (opens in a new tab) - Machine learning made easy

• Trevor Stephens Blog (opens in a new tab) - Trevor Stephens Personal Page

• no free hunch | kaggle (opens in a new tab) - The Kaggle Blog about all things Data Science

• A Quantitative Journey | outlace (opens in a new tab) - learning quantitative applications

• r4stats (opens in a new tab) - analyze the world of data science, and to help people learn to use R

• Variance Explained (opens in a new tab) - David Robinson's Blog

• AI Junkie (opens in a new tab) - a blog about Artificial Intellingence

• Deep Learning Blog by Tim Dettmers (opens in a new tab) - Making deep learning accessible

• J Alammar's Blog (opens in a new tab)- Blog posts about Machine Learning and Neural Nets

• Adam Geitgey (opens in a new tab) - Easiest Introduction to machine learning

• Ethen's Notebook Collection (opens in a new tab) - 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 (opens in a new tab)

• Data Science Topic on Quora (opens in a new tab)

• William Chen's Answers (opens in a new tab)

• Michael Hochster's Answers (opens in a new tab)

• Ricardo Vladimiro's Answers (opens in a new tab)

• Storytelling with Statistics (opens in a new tab)

• Data Science FAQs on Quora (opens in a new tab)

• Machine Learning FAQs on Quora (opens in a new tab)

Kaggle Competitions WriteUp

• How to almost win Kaggle Competitions (opens in a new tab)

• Convolution Neural Networks for EEG detection (opens in a new tab)

• Facebook Recruiting III Explained (opens in a new tab)

• Predicting CTR with Online ML (opens in a new tab)

• How to Rank 10% in Your First Kaggle Competition (opens in a new tab)

Cheat Sheets

• Probability Cheat Sheet (opens in a new tab), Source (opens in a new tab)

• Machine Learning Cheat Sheet (opens in a new tab)

• ML Compiled (opens in a new tab)

Classification

• Does Balancing Classes Improve Classifier Performance? (opens in a new tab)

• What is Deviance? (opens in a new tab)

• When to choose which machine learning classifier? (opens in a new tab)

• What are the advantages of different classification algorithms? (opens in a new tab)

• ROC and AUC Explained (opens in a new tab) (related video (opens in a new tab))

• An introduction to ROC analysis (opens in a new tab)

• Simple guide to confusion matrix terminology (opens in a new tab)

Linear Regression

• General

  • Assumptions of Linear Regression (opens in a new tab), Stack Exchange (opens in a new tab)

  • Linear Regression Comprehensive Resource (opens in a new tab)

  • Applying and Interpreting Linear Regression (opens in a new tab)

  • What does having constant variance in a linear regression model mean? (opens in a new tab)

  • Difference between linear regression on y with x and x with y (opens in a new tab)

  • Is linear regression valid when the dependant variable is not normally distributed? (opens in a new tab)

• Multicollinearity and VIF

  • Dummy Variable Trap | Multicollinearity (opens in a new tab)

  • Dealing with multicollinearity using VIFs (opens in a new tab)

• Residual Analysis

  • Interpreting plot.lm() in R (opens in a new tab)

  • How to interpret a QQ plot? (opens in a new tab)

  • Interpreting Residuals vs Fitted Plot (opens in a new tab)

• Outliers

  • How should outliers be dealt with? (opens in a new tab)

• Elastic Net (opens in a new tab) - Regularization and Variable Selection via the Elastic Net (opens in a new tab)

Logistic Regression

• Logistic Regression Wiki (opens in a new tab)

• Geometric Intuition of Logistic Regression (opens in a new tab)

• Obtaining predicted categories (choosing threshold) (opens in a new tab)

• Residuals in logistic regression (opens in a new tab)

• Difference between logit and probit models (opens in a new tab), Logistic Regression Wiki (opens in a new tab), Probit Model Wiki (opens in a new tab)

• Pseudo R2 for Logistic Regression (opens in a new tab), How to calculate (opens in a new tab), Other Details (opens in a new tab)

• Guide to an in-depth understanding of logistic regression (opens in a new tab)

Model Validation using Resampling

• Resampling Explained (opens in a new tab)

• Partioning data set in R (opens in a new tab)

• Implementing hold-out Validaion in R (opens in a new tab), 2 (opens in a new tab)

• Cross Validation (opens in a new tab)

  • How to use cross-validation in predictive modeling (opens in a new tab)

  • Training with Full dataset after CV? (opens in a new tab)

  • Which CV method is best? (opens in a new tab)

  • Variance Estimates in k-fold CV (opens in a new tab)

  • Is CV a subsitute for Validation Set? (opens in a new tab)

  • Choice of k in k-fold CV (opens in a new tab)

  • CV for ensemble learning (opens in a new tab)

  • k-fold CV in R (opens in a new tab)

  • Good Resources (opens in a new tab)

  • Overfitting and Cross Validation

    • Preventing Overfitting the Cross Validation Data | Andrew Ng (opens in a new tab)

    • Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation (opens in a new tab)

    • CV for detecting and preventing Overfitting (opens in a new tab)

    • How does CV overcome the Overfitting Problem (opens in a new tab)

• Bootstrapping (opens in a new tab)

  • Why Bootstrapping Works? (opens in a new tab)

  • Good Animation (opens in a new tab)

  • Example of Bootstapping (opens in a new tab)

  • Understanding Bootstapping for Validation and Model Selection (opens in a new tab)

  • Cross Validation vs Bootstrap to estimate prediction error (opens in a new tab), Cross-validation vs .632 bootstrapping to evaluate classification performance (opens in a new tab)

Deep Learning

• fast.ai - Practical Deep Learning For Coders (opens in a new tab)

• fast.ai - Cutting Edge Deep Learning For Coders (opens in a new tab)

• A curated list of awesome Deep Learning tutorials, projects and communities (opens in a new tab)

• Deep Learning Papers Reading Roadmap (opens in a new tab)

• Lots of Deep Learning Resources (opens in a new tab)

• Interesting Deep Learning and NLP Projects (Stanford) (opens in a new tab), Website (opens in a new tab)

• Core Concepts of Deep Learning (opens in a new tab)

• Understanding Natural Language with Deep Neural Networks Using Torch (opens in a new tab)

• Stanford Deep Learning Tutorial (opens in a new tab)

• Deep Learning FAQs on Quora (opens in a new tab)

• Google+ Deep Learning Page (opens in a new tab)

• Recent Reddit AMAs related to Deep Learning (opens in a new tab), Another AMA (opens in a new tab)

• Where to Learn Deep Learning? (opens in a new tab)

• Deep Learning nvidia concepts (opens in a new tab)

• Introduction to Deep Learning Using Python (GitHub) (opens in a new tab), Good Introduction Slides (opens in a new tab)

• Video Lectures Oxford 2015 (opens in a new tab), Video Lectures Summer School Montreal (opens in a new tab)

• Deep Learning Software List (opens in a new tab)

• Hacker's guide to Neural Nets (opens in a new tab)

• Top arxiv Deep Learning Papers explained (opens in a new tab)

• Geoff Hinton Youtube Vidoes on Deep Learning (opens in a new tab)

• Awesome Deep Learning Reading List (opens in a new tab)

• Deep Learning Comprehensive Website (opens in a new tab), Software (opens in a new tab)

• deeplearning Tutorials (opens in a new tab)

• AWESOME! Deep Learning Tutorial (opens in a new tab)

• Deep Learning Basics (opens in a new tab)

• Intuition Behind Backpropagation (opens in a new tab)

• Stanford Tutorials (opens in a new tab)

• Train, Validation & Test in Artificial Neural Networks (opens in a new tab)

• Artificial Neural Networks Tutorials (opens in a new tab)

• Neural Networks FAQs on Stack Overflow (opens in a new tab)

• Deep Learning Tutorials on deeplearning.net (opens in a new tab)

• Neural Networks and Deep Learning Online Book (opens in a new tab)

• Neural Machine Translation

  • Machine Translation Reading List (opens in a new tab)

  • Introduction to Neural Machine Translation with GPUs (part 1) (opens in a new tab), Part 2 (opens in a new tab), Part 3 (opens in a new tab)

  • Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning (opens in a new tab)

• Deep Learning Frameworks

  • Torch vs. Theano (opens in a new tab)

  • dl4j vs. torch7 vs. theano (opens in a new tab)

  • Deep Learning Libraries by Language (opens in a new tab)

  • Theano (opens in a new tab)

    • Website (opens in a new tab)

    • Theano Introduction (opens in a new tab)

    • Theano Tutorial (opens in a new tab)

    • Good Theano Tutorial (opens in a new tab)

    • Logistic Regression using Theano for classifying digits (opens in a new tab)

    • MLP using Theano (opens in a new tab)

    • CNN using Theano (opens in a new tab)

    • RNNs using Theano (opens in a new tab)

    • LSTM for Sentiment Analysis in Theano (opens in a new tab)

    • RBM using Theano (opens in a new tab)

    • DBNs using Theano (opens in a new tab)

    • All Codes (opens in a new tab)

    • Deep Learning Implementation Tutorials - Keras and Lasagne (opens in a new tab)

  • Torch (opens in a new tab)

    • Torch ML Tutorial (opens in a new tab), Code (opens in a new tab)

    • Intro to Torch (opens in a new tab)

    • Learning Torch GitHub Repo (opens in a new tab)

    • Awesome-Torch (Repository on GitHub) (opens in a new tab)

    • Machine Learning using Torch Oxford Univ (opens in a new tab), Code (opens in a new tab)

    • Torch Internals Overview (opens in a new tab)

    • Torch Cheatsheet (opens in a new tab)

    • Understanding Natural Language with Deep Neural Networks Using Torch (opens in a new tab)

  • Caffe

    • Deep Learning for Computer Vision with Caffe and cuDNN (opens in a new tab)

  • TensorFlow

    • Website (opens in a new tab)

    • TensorFlow Examples for Beginners (opens in a new tab)

    • Stanford Tensorflow for Deep Learning Research Course (opens in a new tab)

      • GitHub Repo (opens in a new tab)

    • Simplified Scikit-learn Style Interface to TensorFlow (opens in a new tab)

    • Learning TensorFlow GitHub Repo (opens in a new tab)

    • Benchmark TensorFlow GitHub (opens in a new tab)

    • Awesome TensorFlow List (opens in a new tab)

    • TensorFlow Book (opens in a new tab)

    • Android TensorFlow Machine Learning Example (opens in a new tab)

      • GitHub Repo (opens in a new tab)

    • Creating Custom Model For Android Using TensorFlow (opens in a new tab)

      • GitHub Repo (opens in a new tab)

• Feed Forward Networks

  • A Quick Introduction to Neural Networks (opens in a new tab)

  • Implementing a Neural Network from scratch (opens in a new tab), Code (opens in a new tab)

  • Speeding up your Neural Network with Theano and the gpu (opens in a new tab), Code (opens in a new tab)

  • Basic ANN Theory (opens in a new tab)

  • Role of Bias in Neural Networks (opens in a new tab)

  • Choosing number of hidden layers and nodes (opens in a new tab),2 (opens in a new tab),3 (opens in a new tab)

  • Backpropagation in Matrix Form (opens in a new tab)

  • ANN implemented in C++ | AI Junkie (opens in a new tab)

  • Simple Implementation (opens in a new tab)

  • NN for Beginners (opens in a new tab)

  • Regression and Classification with NNs (Slides) (opens in a new tab)

  • Another Intro (opens in a new tab)

• Recurrent and LSTM Networks

  • awesome-rnn: list of resources (GitHub Repo) (opens in a new tab)

  • Recurrent Neural Net Tutorial Part 1 (opens in a new tab), Part 2 (opens in a new tab), Part 3 (opens in a new tab), Code (opens in a new tab)

  • NLP RNN Representations (opens in a new tab)

  • The Unreasonable effectiveness of RNNs (opens in a new tab), Torch Code (opens in a new tab), Python Code (opens in a new tab)

  • Intro to RNN (opens in a new tab), LSTM (opens in a new tab)

  • An application of RNN (opens in a new tab)

  • Optimizing RNN Performance (opens in a new tab)

  • Simple RNN (opens in a new tab)

  • Auto-Generating Clickbait with RNN (opens in a new tab)

  • Sequence Learning using RNN (Slides) (opens in a new tab)

  • Machine Translation using RNN (Paper) (opens in a new tab)

  • Music generation using RNNs (Keras) (opens in a new tab)

  • Using RNN to create on-the-fly dialogue (Keras) (opens in a new tab)

  • Long Short Term Memory (LSTM)

    • Understanding LSTM Networks (opens in a new tab)

    • LSTM explained (opens in a new tab)

    • Beginner’s Guide to LSTM (opens in a new tab)

    • Implementing LSTM from scratch (opens in a new tab), Python/Theano code (opens in a new tab)

    • Torch Code for character-level language models using LSTM (opens in a new tab)

    • LSTM for Kaggle EEG Detection competition (Torch Code) (opens in a new tab)

    • LSTM for Sentiment Analysis in Theano (opens in a new tab)

    • Deep Learning for Visual Q&A | LSTM | CNN (opens in a new tab), Code (opens in a new tab)

    • Computer Responds to email using LSTM | Google (opens in a new tab)

    • LSTM dramatically improves Google Voice Search (opens in a new tab), Another Article (opens in a new tab)

    • Understanding Natural Language with LSTM Using Torch (opens in a new tab)

    • Torch code for Visual Question Answering using a CNN+LSTM model (opens in a new tab)

    • LSTM for Human Activity Recognition (opens in a new tab)

  • Gated Recurrent Units (GRU)

    • LSTM vs GRU (opens in a new tab)

  • Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models (opens in a new tab)

• Recursive Neural Network (not Recurrent) (opens in a new tab)

  • Recursive Neural Tensor Network (RNTN) (opens in a new tab)

  • word2vec, DBN, RNTN for Sentiment Analysis (opens in a new tab)

• Restricted Boltzmann Machine

  • Beginner's Guide about RBMs (opens in a new tab)

  • Another Good Tutorial (opens in a new tab)

  • Introduction to RBMs (opens in a new tab)

  • Hinton's Guide to Training RBMs (opens in a new tab)

  • RBMs in R (opens in a new tab)

  • Deep Belief Networks Tutorial (opens in a new tab)

  • word2vec, DBN, RNTN for Sentiment Analysis (opens in a new tab)

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

  • Andrew Ng Sparse Autoencoders pdf (opens in a new tab)

  • Deep Autoencoders Tutorial (opens in a new tab)

  • Denoising Autoencoders (opens in a new tab), Theano Code (opens in a new tab)

  • Stacked Denoising Autoencoders (opens in a new tab)

• Convolutional Neural Networks

  • An Intuitive Explanation of Convolutional Neural Networks (opens in a new tab)

  • Awesome Deep Vision: List of Resources (GitHub) (opens in a new tab)

  • Intro to CNNs (opens in a new tab)

  • Understanding CNN for NLP (opens in a new tab)

  • Stanford Notes (opens in a new tab), Codes (opens in a new tab), GitHub (opens in a new tab)

  • JavaScript Library (Browser Based) for CNNs (opens in a new tab)

  • Using CNNs to detect facial keypoints (opens in a new tab)

  • Deep learning to classify business photos at Yelp (opens in a new tab)

  • Interview with Yann LeCun | Kaggle (opens in a new tab)

  • Visualising and Understanding CNNs (opens in a new tab)

• Network Representation Learning

  • Awesome Graph Embedding (opens in a new tab)

  • Awesome Network Embedding (opens in a new tab)

  • Network Representation Learning Papers (opens in a new tab)

  • Knowledge Representation Learning Papers (opens in a new tab)

  • Graph Based Deep Learning Literature (opens in a new tab)

Natural Language Processing

• A curated list of speech and natural language processing resources (opens in a new tab)

• Understanding Natural Language with Deep Neural Networks Using Torch (opens in a new tab)

• tf-idf explained (opens in a new tab)

• Interesting Deep Learning NLP Projects Stanford (opens in a new tab), Website (opens in a new tab)

• The Stanford NLP Group (opens in a new tab)

• NLP from Scratch | Google Paper (opens in a new tab)

• Graph Based Semi Supervised Learning for NLP (opens in a new tab)

• Bag of Words (opens in a new tab)

  • Classification text with Bag of Words (opens in a new tab)

• Topic Modeling

  • Topic Modeling Wikipedia (opens in a new tab)

  • Probabilistic Topic Models Princeton PDF (opens in a new tab)

  • LDA Wikipedia (opens in a new tab), LSA Wikipedia (opens in a new tab), Probabilistic LSA Wikipedia (opens in a new tab)

  • What is a good explanation of Latent Dirichlet Allocation (LDA)? (opens in a new tab)

  • Introduction to LDA (opens in a new tab), Another good explanation (opens in a new tab)

  • The LDA Buffet - Intuitive Explanation (opens in a new tab)

  • Your Guide to Latent Dirichlet Allocation (LDA) (opens in a new tab)

  • Difference between LSI and LDA (opens in a new tab)

  • Original LDA Paper (opens in a new tab)

  • alpha and beta in LDA (opens in a new tab)

  • Intuitive explanation of the Dirichlet distribution (opens in a new tab)

  • topicmodels: An R Package for Fitting Topic Models (opens in a new tab)

  • Topic modeling made just simple enough (opens in a new tab)

  • Online LDA (opens in a new tab), Online LDA with Spark (opens in a new tab)

  • LDA in Scala (opens in a new tab), Part 2 (opens in a new tab)

  • Segmentation of Twitter Timelines via Topic Modeling (opens in a new tab)

  • Topic Modeling of Twitter Followers (opens in a new tab)

  • Multilingual Latent Dirichlet Allocation (LDA) (opens in a new tab). (Tutorial here (opens in a new tab))

  • Deep Belief Nets for Topic Modeling (opens in a new tab)

  • Gaussian LDA for Topic Models with Word Embeddings (opens in a new tab)

  • Python

    • Series of lecture notes for probabilistic topic models written in ipython notebook (opens in a new tab)
    • Implementation of various topic models in Python (opens in a new tab)

• word2vec

  • Google word2vec (opens in a new tab)

  • Bag of Words Model Wiki (opens in a new tab)

  • word2vec Tutorial (opens in a new tab)

  • A closer look at Skip Gram Modeling (opens in a new tab)

  • Skip Gram Model Tutorial (opens in a new tab), CBoW Model (opens in a new tab)

  • Word Vectors Kaggle Tutorial Python (opens in a new tab), Part 2 (opens in a new tab)

  • Making sense of word2vec (opens in a new tab)

  • word2vec explained on deeplearning4j (opens in a new tab)

  • Quora word2vec (opens in a new tab)

  • Other Quora Resources (opens in a new tab), 2 (opens in a new tab), 3 (opens in a new tab)

  • word2vec, DBN, RNTN for Sentiment Analysis (opens in a new tab)

• Text Clustering

  • How string clustering works (opens in a new tab)

  • Levenshtein distance for measuring the difference between two sequences (opens in a new tab)

  • Text clustering with Levenshtein distances (opens in a new tab)

• Text Classification

  • Classification Text with Bag of Words (opens in a new tab)

• Named Entity Recognitation

  • Stanford Named Entity Recognizer (NER) (opens in a new tab)

  • Named Entity Recognition: Applications and Use Cases- Towards Data Science (opens in a new tab)

• Language learning with NLP and reinforcement learning (opens in a new tab)

• Kaggle Tutorial Bag of Words and Word vectors (opens in a new tab), Part 2 (opens in a new tab), Part 3 (opens in a new tab)

• What would Shakespeare say (NLP Tutorial) (opens in a new tab)

• A closer look at Skip Gram Modeling (opens in a new tab)

Computer Vision

• Awesome computer vision (github) (opens in a new tab)

• Awesome deep vision (github) (opens in a new tab)

Support Vector Machine

• Highest Voted Questions about SVMs on Cross Validated (opens in a new tab)

• Help me Understand SVMs! (opens in a new tab)

• SVM in Layman's terms (opens in a new tab)

• How does SVM Work | Comparisons (opens in a new tab)

• A tutorial on SVMs (opens in a new tab)

• Practical Guide to SVC (opens in a new tab), Slides (opens in a new tab)

• Introductory Overview of SVMs (opens in a new tab)

• Comparisons

  • SVMs > ANNs (opens in a new tab), ANNs > SVMs (opens in a new tab), Another Comparison (opens in a new tab)

  • Trees > SVMs (opens in a new tab)

  • Kernel Logistic Regression vs SVM (opens in a new tab)

  • Logistic Regression vs SVM (opens in a new tab), 2 (opens in a new tab), 3 (opens in a new tab)

• Optimization Algorithms in Support Vector Machines (opens in a new tab)

• Variable Importance from SVM (opens in a new tab)

• Software

  • LIBSVM (opens in a new tab)

  • Intro to SVM in R (opens in a new tab)

• Kernels

  • What are Kernels in ML and SVM? (opens in a new tab)

  • Intuition Behind Gaussian Kernel in SVMs? (opens in a new tab)

• Probabilities post SVM

  • Platt's Probabilistic Outputs for SVM (opens in a new tab)

  • Platt Calibration Wiki (opens in a new tab)

  • Why use Platts Scaling (opens in a new tab)

  • Classifier Classification with Platt's Scaling (opens in a new tab)

Reinforcement Learning

• Awesome Reinforcement Learning (GitHub) (opens in a new tab)

• RL Tutorial Part 1 (opens in a new tab), Part 2 (opens in a new tab)

Decision Trees

• Wikipedia Page - Lots of Good Info (opens in a new tab)

• FAQs about Decision Trees (opens in a new tab)

• Brief Tour of Trees and Forests (opens in a new tab)

• Tree Based Models in R (opens in a new tab)

• How Decision Trees work? (opens in a new tab)

• Weak side of Decision Trees (opens in a new tab)

• Thorough Explanation and different algorithms (opens in a new tab)

• What is entropy and information gain in the context of building decision trees? (opens in a new tab)

• Slides Related to Decision Trees (opens in a new tab)

• How do decision tree learning algorithms deal with missing values? (opens in a new tab)

• Using Surrogates to Improve Datasets with Missing Values (opens in a new tab)

• Good Article (opens in a new tab)

• Are decision trees almost always binary trees? (opens in a new tab)

• Pruning Decision Trees (opens in a new tab), Grafting of Decision Trees (opens in a new tab)

• What is Deviance in context of Decision Trees? (opens in a new tab)

• Discover structure behind data with decision trees (opens in a new tab) - Grow and plot a decision tree to automatically figure out hidden rules in your data

• Comparison of Different Algorithms

  • CART vs CTREE (opens in a new tab)

  • Comparison of complexity or performance (opens in a new tab)

  • CHAID vs CART (opens in a new tab) , CART vs CHAID (opens in a new tab)

  • Good Article on comparison (opens in a new tab)

• CART

  • Recursive Partitioning Wikipedia (opens in a new tab)

  • CART Explained (opens in a new tab)

  • How to measure/rank “variable importance” when using CART? (opens in a new tab)

  • Pruning a Tree in R (opens in a new tab)

  • Does rpart use multivariate splits by default? (opens in a new tab)

  • FAQs about Recursive Partitioning (opens in a new tab)

• CTREE

  • party package in R (opens in a new tab)

  • Show volumne in each node using ctree in R (opens in a new tab)

  • How to extract tree structure from ctree function? (opens in a new tab)

• CHAID

  • Wikipedia Artice on CHAID (opens in a new tab)

  • Basic Introduction to CHAID (opens in a new tab)

  • Good Tutorial on CHAID (opens in a new tab)

• MARS

  • Wikipedia Article on MARS (opens in a new tab)

• Probabilistic Decision Trees

  • Bayesian Learning in Probabilistic Decision Trees (opens in a new tab)

  • Probabilistic Trees Research Paper (opens in a new tab)

Random Forest / Bagging

• Awesome Random Forest (GitHub)** (opens in a new tab)

• How to tune RF parameters in practice? (opens in a new tab)

• Measures of variable importance in random forests (opens in a new tab)

• Compare R-squared from two different Random Forest models (opens in a new tab)

• OOB Estimate Explained | RF vs LDA (opens in a new tab)

• Evaluating Random Forests for Survival Analysis Using Prediction Error Curve (opens in a new tab)

• Why doesn't Random Forest handle missing values in predictors? (opens in a new tab)

• How to build random forests in R with missing (NA) values? (opens in a new tab)

• FAQs about Random Forest (opens in a new tab), More FAQs (opens in a new tab)

• Obtaining knowledge from a random forest (opens in a new tab)

• Some Questions for R implementation (opens in a new tab), 2 (opens in a new tab), 3 (opens in a new tab)

Boosting

• Boosting for Better Predictions (opens in a new tab)

• Boosting Wikipedia Page (opens in a new tab)

• Introduction to Boosted Trees | Tianqi Chen (opens in a new tab)

• Gradient Boosting Machine

  • Gradiet Boosting Wiki (opens in a new tab)

  • Guidelines for GBM parameters in R (opens in a new tab), Strategy to set parameters (opens in a new tab)

  • Meaning of Interaction Depth (opens in a new tab), 2 (opens in a new tab)

  • Role of n.minobsinnode parameter of GBM in R (opens in a new tab)

  • GBM in R (opens in a new tab)

  • FAQs about GBM (opens in a new tab)

  • GBM vs xgboost (opens in a new tab)

• xgboost

  • xgboost tuning kaggle (opens in a new tab)

  • xgboost vs gbm (opens in a new tab)

  • xgboost survey (opens in a new tab)

  • Practical XGBoost in Python online course (free) (opens in a new tab)

• AdaBoost

  • AdaBoost Wiki (opens in a new tab), Python Code (opens in a new tab)

  • AdaBoost Sparse Input Support (opens in a new tab)

  • adaBag R package (opens in a new tab)

  • Tutorial (opens in a new tab)

• CatBoost

  • CatBoost Documentation (opens in a new tab)

  • Benchmarks (opens in a new tab)

  • Tutorial (opens in a new tab)

  • GitHub Project (opens in a new tab)

  • CatBoost vs. Light GBM vs. XGBoost (opens in a new tab)

Ensembles

• Wikipedia Article on Ensemble Learning (opens in a new tab)

• Kaggle Ensembling Guide (opens in a new tab)

• The Power of Simple Ensembles (opens in a new tab)

• Ensemble Learning Intro (opens in a new tab)

• Ensemble Learning Paper (opens in a new tab)

• Ensembling models with R (opens in a new tab), Ensembling Regression Models in R (opens in a new tab), Intro to Ensembles in R (opens in a new tab)

• Ensembling Models with caret (opens in a new tab)

• Bagging vs Boosting vs Stacking (opens in a new tab)

• Good Resources | Kaggle Africa Soil Property Prediction (opens in a new tab)

• Boosting vs Bagging (opens in a new tab)

• Resources for learning how to implement ensemble methods (opens in a new tab)

• How are classifications merged in an ensemble classifier? (opens in a new tab)

Stacking Models

• Stacking, Blending and Stacked Generalization (opens in a new tab)

• Stacked Generalization (Stacking) (opens in a new tab)

• Stacked Generalization: when does it work? (opens in a new tab)

• Stacked Generalization Paper (opens in a new tab)

Vapnik–Chervonenkis Dimension

• Wikipedia article on VC Dimension (opens in a new tab)

• Intuitive Explanantion of VC Dimension (opens in a new tab)

• Video explaining VC Dimension (opens in a new tab)

• Introduction to VC Dimension (opens in a new tab)

• FAQs about VC Dimension (opens in a new tab)

• Do ensemble techniques increase VC-dimension? (opens in a new tab)

Bayesian Machine Learning

• Bayesian Methods for Hackers (using pyMC) (opens in a new tab)

• Should all Machine Learning be Bayesian? (opens in a new tab)

• Tutorial on Bayesian Optimisation for Machine Learning (opens in a new tab)

• Bayesian Reasoning and Deep Learning (opens in a new tab), Slides (opens in a new tab)

• Bayesian Statistics Made Simple (opens in a new tab)

• Kalman & Bayesian Filters in Python (opens in a new tab)

• Markov Chain Wikipedia Page (opens in a new tab)

Semi Supervised Learning

• Wikipedia article on Semi Supervised Learning (opens in a new tab)

• Tutorial on Semi Supervised Learning (opens in a new tab)

• Graph Based Semi Supervised Learning for NLP (opens in a new tab)

• Taxonomy (opens in a new tab)

• Video Tutorial Weka (opens in a new tab)

• Unsupervised, Supervised and Semi Supervised learning (opens in a new tab)

• Research Papers 1 (opens in a new tab), 2 (opens in a new tab), 3 (opens in a new tab)

Optimization

• Mean Variance Portfolio Optimization with R and Quadratic Programming (opens in a new tab)

• Algorithms for Sparse Optimization and Machine Learning (opens in a new tab)

• Optimization Algorithms in Machine Learning (opens in a new tab), Video Lecture (opens in a new tab)

• Optimization Algorithms for Data Analysis (opens in a new tab)

• Video Lectures on Optimization (opens in a new tab)

• Optimization Algorithms in Support Vector Machines (opens in a new tab)

• The Interplay of Optimization and Machine Learning Research (opens in a new tab)

• Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters (opens in a new tab)

Other Tutorials

• For a collection of Data Science Tutorials using R, please refer to this list (opens in a new tab).

• For a collection of Data Science Tutorials using Python, please refer to this list (opens in a new tab).

Save Cheat Sheets!

• The best Cheat Sheets for Artificial Intelligence, Machine Learning, and Python.
  • Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data (opens in a new tab) - Stefan Kojouharov
  • Machine Learning cheatsheets for Stanford's CS 229 (opens in a new tab) - Afshine Amidi & Shervine Amidi
  • Cheat Sheet of Machine Learning and Python (and Math) Cheat Sheets (opens in a new tab) - Robbie Allen
  • AI Expert Roadmap (opens in a new tab) - Use it as a skillset checklist!

How to get started with ML

2. Learn Python

• 4h Beginner Course (opens in a new tab)
• 6h Intermediate Python Programming Course (opens in a new tab)
• Learn Python In X minutes (opens in a new tab)
• Cheatsheet (opens in a new tab)

3. Learn The ML Tech Stack:

1. NumPy:
   • 1h NumPy Crash Course (opens in a new tab)
   • Numpy101 zerotomastery (opens in a new tab)
2. Pandas:
   • 1h Pandas Crash Course (opens in a new tab)
   • Pandas101 zerotomastery (opens in a new tab)
3. Matplotlib:
   • 1h Matplotlib Crash Course Course (opens in a new tab)
   • Matplotlib zerotomastery (opens in a new tab)

(Scikit-Learn and TensorFlow are taught in step 4. PyTorch is optional, maybe in step 7)

4. Machine Learning Courses

• Machine Learning Specialization Andrew Ng | Coursera (opens in a new tab) (3 Courses)
• Optional: Machine Learning From Scratch (opens in a new tab)

5. Hands-on Data Preparation

• Kaggle Intro to Machine Learning (opens in a new tab)
• Kaggle Intermediate Machine Learning (opens in a new tab)

Future [Specialize & Create Blog]

• Specialize in one field (e.g. Computer Vision, NLP, etc.)
• Look at requirements in corresponding job descriptions and learn those skills
• Tip: Create a blog and share tutorials and what you have learned!

Additional Resources