Machine-Learning-Tutorials
machine learning and deep learning tutorials, articles and other resources
https://github.com/ujjwalkarn/Machine-Learning-Tutorials
Last synced: 15 days ago
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Logistic Regression
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Model Validation using Resampling
- Resampling Explained
- Partioning data set in R
- Implementing hold-out Validaion in R - data-frame-into-testing-and.html)
- Cross Validation
- How to use cross-validation in predictive modeling
- 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
- 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 - validation vs .632 bootstrapping to evaluate classification performance](http://stats.stackexchange.com/questions/71184/cross-validation-or-bootstrapping-to-evaluate-classification-performance)
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- Preventing Overfitting the Cross Validation Data | Andrew Ng
- Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
- Example of Bootstapping
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Natural Language Processing
- Interesting Deep Learning NLP Projects Stanford
- A curated list of speech and natural language processing resources
- tf-idf explained
- The Stanford NLP Group
- NLP from Scratch | Google Paper
- Bag of Words Model Wiki
- Classification Text with Bag of Words
- Topic Modeling Wikipedia
- **Probabilistic Topic Models Princeton PDF**
- LDA Wikipedia
- What is a good explanation of Latent Dirichlet Allocation (LDA)?
- **Introduction to LDA** - boyd-graber-and-philip-resnik.html)
- The LDA Buffet - Intuitive Explanation
- Your Guide to Latent Dirichlet Allocation (LDA)
- Difference between LSI and LDA
- Original LDA Paper
- alpha and beta in LDA
- Intuitive explanation of the Dirichlet distribution
- topicmodels: An R Package for Fitting Topic Models
- Topic modeling made just simple enough
- Online LDA - online-latent-dirichlet-allocation-with-apache-spark.html)
- LDA in Scala - dirichlet-allocation-in-scala-part-ii-the-code.html)
- Segmentation of Twitter Timelines via Topic Modeling
- Topic Modeling of Twitter Followers
- Gaussian LDA for Topic Models with Word Embeddings
- Google word2vec
- A closer look at Skip Gram Modeling
- Skip Gram Model Tutorial - tutorial-part-ii-the-continuous-bag-of-words-model.html)
- Word Vectors Kaggle Tutorial Python - nlp-tutorial/details/part-3-more-fun-with-word-vectors)
- Quora word2vec
- Other Quora Resources - is-the-difference-between-the-Bag-of-Words-model-and-the-Continuous-Bag-of-Words-model), [3](https://www.quora.com/Is-skip-gram-negative-sampling-better-than-CBOW-NS-for-word2vec-If-so-why)
- How string clustering works
- Levenshtein distance for measuring the difference between two sequences
- Text clustering with Levenshtein distances
- Stanford Named Entity Recognizer (NER)
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Language learning with NLP and reinforcement learning
- What would Shakespeare say (NLP Tutorial)
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- tf-idf explained
- Graph Based Semi Supervised Learning for NLP
- Classification text with Bag of Words
- **Probabilistic Topic Models Princeton PDF**
- **Introduction to LDA** - boyd-graber-and-philip-resnik.html)
- Multilingual Latent Dirichlet Allocation (LDA) - Latent-Dirichlet-Allocation-LDA/blob/master/Multilingual-LDA-Pipeline-Tutorial.ipynb))
- Deep Belief Nets for Topic Modeling
- Gaussian LDA for Topic Models with Word Embeddings
- Series of lecture notes for probabilistic topic models written in ipython notebook
- Implementation of various topic models in Python
- Quora word2vec
- Other Quora Resources - is-the-difference-between-the-Bag-of-Words-model-and-the-Continuous-Bag-of-Words-model), [3](https://www.quora.com/Is-skip-gram-negative-sampling-better-than-CBOW-NS-for-word2vec-If-so-why)
- word2vec explained on deeplearning4j
- word2vec Tutorial
- Making sense of word2vec
- Word Vectors Kaggle Tutorial Python - nlp-tutorial/details/part-3-more-fun-with-word-vectors)
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Kaggle Tutorial Bag of Words and Word vectors - nlp-tutorial/details/part-2-word-vectors), [Part 3](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors)
- What would Shakespeare say (NLP Tutorial)
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Optimization
- Optimization Algorithms in Support Vector Machines
- Mean Variance Portfolio Optimization with R and Quadratic Programming
- Algorithms for Sparse Optimization and Machine Learning
- Optimization Algorithms in Machine Learning
- Optimization Algorithms for Data Analysis
- Video Lectures on Optimization
- The Interplay of Optimization and Machine Learning Research
- Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters
- Optimization Algorithms for Data Analysis
- Video Lectures on Optimization
- The Interplay of Optimization and Machine Learning Research
- Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters
- Optimization Algorithms in Machine Learning
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Random Forest / Bagging
- 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 - forest)
- Obtaining knowledge from a random forest
- Some Questions for R implementation - preprocessing-is-needed-before-prediction-using-finalmodel-of-randomfore), [3](http://stackoverflow.com/questions/17059432/random-forest-package-in-r-shows-error-during-prediction-if-there-are-new-fact)
- Awesome Random Forest (GitHub)**
- Measures of variable importance in random forests
- Compare R-squared from two different Random Forest models
- Why doesn't Random Forest handle missing values in predictors?
- FAQs about Random Forest - forest)
- Obtaining knowledge from a random forest
- How to tune RF parameters in practice?
- Evaluating Random Forests for Survival Analysis Using Prediction Error Curve
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Reinforcement Learning
- RL Tutorial Part 1 - Learning-Part-2/)
- Awesome Reinforcement Learning (GitHub)
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Resources on Quora
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Semi Supervised Learning
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Stacking Models
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Statistics
- Stat Trek Website - A dedicated website to teach yourselves Statistics
- Statistics for Hackers | Slides | @jakevdp - Slides by Jake VanderPlas
- Online Statistics Book - An Interactive Multimedia Course for Studying Statistics
- What is a Sampling Distribution?
- AP Statistics Tutorial
- Matrix Algebra Tutorial
- What is an Unbiased Estimator?
- Goodness of Fit Explained
- What are QQ Plots?
- OpenIntro Statistics - Free PDF textbook
- Learn Statistics Using Python - Learn Statistics using an application-centric programming approach
- What are QQ Plots?
- OpenIntro Statistics - Free PDF textbook
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Support Vector Machine
Categories
Deep Learning
154
Natural Language Processing
88
Boosting
65
Decision Trees
52
Model Validation using Resampling
40
Introduction
30
Support Vector Machine
30
Useful Blogs
25
Ensembles
21
Random Forest / Bagging
18
Linear Regression
16
Optimization
13
Statistics
13
Resources on Quora
10
Bayesian Machine Learning
10
Vapnik–Chervonenkis Dimension
9
Artificial Intelligence
8
Genetic Algorithms
8
Kaggle Competitions WriteUp
8
Semi Supervised Learning
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Logistic Regression
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Interview Resources
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Classification
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Stacking Models
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Cheat Sheets
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