Machine Learning - Tutorials
Contents_Index
- GENERAL2
- INTRODUCTION26
- INTERVIEW RESOURCES6
- ARTIFICIAL INTELLIGENCE7
- GENETIC ALGORITHMS5
- STATISTICS9
- USEFUL BLOGS20
- RESOURCES ON QUORA8
- KAGGLE COMPETITIONS WRITEUP5
- CHEAT SHEETS3
- CLASSIFICATION7
- LINEAR REGRESSION1
- LOGISTIC REGRESSION7
- MODEL VALIDATION USING RESAMPLING5
- DEEP LEARNING36
- NATURAL LANGUAGE PROCESSING14
- COMPUTER VISION2
- SUPPORT VECTOR MACHINE9
- REINFORCEMENT LEARNING2
- DECISION TREES16
- RANDOM FOREST / BAGGING11
- BOOSTING3
- ENSEMBLES12
- STACKING MODELS4
- VAPNIK–CHERVONENKIS DIMENSION6
- BAYESIAN MACHINE LEARNING7
- SEMI SUPERVISED LEARNING7
- OPTIMIZATION8
General
2_ENTRIES-
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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If you want to contribute to this list, please read Contributing Guidelines.
Introduction
26_ENTRIESInterview Resources
6_ENTRIESArtificial Intelligence
7_ENTRIESGenetic Algorithms
5_ENTRIESStatistics
9_ENTRIES- 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
- OpenIntro Statistics
Free PDF textbook
Useful Blogs
20_ENTRIES- 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
8_ENTRIESKaggle Competitions WriteUp
5_ENTRIESCheat Sheets
3_ENTRIES
Classification
7_ENTRIESLinear Regression
1_ENTRIES- GeneralAssumptions of Linear Regression, Stack ExchangeLinear Regression Comprehensive ResourceApplying and Interpreting Linear RegressionWhat 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?](https://www.researchgate.net/post/Is_linear_regression_valid_when_the_outcome_depe…
Logistic Regression
7_ENTRIESModel Validation using Resampling
5_ENTRIESDeep Learning
36_ENTRIES-
Deep Learning FrameworksTorch vs. Theanodl4j vs. torch7 vs. theanoDeep Learning Libraries by LanguageTheanoWebsiteTheano IntroductionTheano TutorialGood Theano TutorialLogistic Regression using Theano for classifying digitsMLP using Theano[CNN using Theano](http://deeplearning.net/tutorial/lenet.html#len…
- Autoencoders
Autoencoders: Unsupervised (applies BackProp after setting target = input)Andrew Ng Sparse Autoencoders pdfDeep Autoencoders TutorialDenoising Autoencoders, Theano CodeStacked Denoising Autoencoders
Natural Language Processing
14_ENTRIES- Topic Modeling[Topic Modeling Wikiped...
Topic ModelingTopic Modeling WikipediaProbabilistic Topic Models Princeton PDFLDA Wikipedia, LSA Wikipedia, Probabilistic LSA Wikipedia[What is a good explanation of Latent Dirichlet Allocation (LDA)?](h…
Computer Vision
2_ENTRIESSupport Vector Machine
9_ENTRIES-
ComparisonsSVMs > ANNs, ANNs > SVMs, Another ComparisonTrees > SVMsKernel Logistic Regression vs SVMLogistic Regression vs SVM, 2, 3
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SoftwareLIBSVM[Intro to SVM in R]…
Reinforcement Learning
2_ENTRIESDecision Trees
16_ENTRIES-
Comparison of Different AlgorithmsCART vs CTREEComparison of complexity or performanceCHAID vs CART , CART vs CHAIDGood Article on comparison
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CARTRecursive Partitioning WikipediaCART ExplainedHow to measure/rank “variable importance” when using CART?[Pruning a Tree in R](htt…
- Discover structure behind data with decision trees
Grow and plot a decision tree to automatically figure out hidden rules in your data
Random Forest / Bagging
11_ENTRIESBoosting
3_ENTRIES-
Gradient Boosting MachineGradiet Boosting WikiGuidelines for GBM parameters in R, Strategy to set parametersMeaning of Interaction Depth, 2Role of n.minobsinnode parameter of GBM in RGBM in RFAQs about GBMGBM vs xgboost
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xgboost[xgboost tuning kaggle](https://www.kaggle.com/khozzy/rossmann-store…