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Statistics/MachineLearning

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Cool blogs

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  1. Learning With Data
  2. Step by step Kaggle competition tutorial

MODELS

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  1. Discover Feature Engineering, How to Engineer Features and How to Get Good at It
  2. 7 Types of Regression Techniques you should know!

XGBOOST

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  1. Flavours of Physics: Finding
  2. Complete Guide to Parameter Tuning in XGBoost (with codes in Python)
  3. How to tune hyperparameters of xgboost trees?
  4. basic walkthrough
  5. kaggle higgs example
  6. Python Package Introduction
  7. 2 more

Classification Error (Error metrics)

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  1. Precision and recall
  2. Precision, recall, sensitivity and specificity
  3. Precision and recall
  4. SKLearn classification metrics
  5. Plot & Intrepret an ROC Curve
  6. The AUC of an ROC Curve

Cross Validating

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  1. training, val, test sets

DecisionTreeClassifier

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  1. Tree module
  2. Gradient boosted trees and other things
  3. Build dec tree from scratch
  4. Entropy (information theory)
  5. Bootstrap aggregating (Bagging)
  6. DTC - from UWisconsin
  7. 2 more

Generalized Linear Model

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  1. Statsmodels: GLM
  2. What is a link function?
  3. Generalized linear model

LogReg

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  1. statsmodels.discrete.discrete_model.Logit — statsmodels 0.6.0 documentation
  2. Statsmodels logreg w/ R-like examples
  3. MORE LOGREG!
  4. Interpret LogReg
  5. Logistic Reg Ex

Maximum Likelihood

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  1. MLE for logisitc regression
  2. Maximum likelihood

NaiveBayes

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  1. thinkbayes.pdf
  2. Bayesian inference
  3. Math behind NB
  4. Spam filtering with NB
  5. Why NB works so well
  6. Naive Bayes classifier
  7. 4 more

OLS

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  1. OLS in python
  2. Problems with R-sq

Poisson

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  1. img compare lin reg to poiss reg
  2. Compare least sq to poisson reg
  3. Poisson Regression
  4. Poisson Distribution
  5. The Poisson Probability Distribution
  6. Poisson point process
  7. 1 more

Random Forest

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  1. Random forest

Regularization

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  1. When should I use lasso vs ridge?
  2. difference between L1 and L2 regularization?
  3. Regularization for regular people

Stochastic Gradient Descent (SGD)

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  1. SGD tricks
  2. Convex loss functions
  3. SGDRegressor
  4. SGDClassifier
  5. Stochastic gradient descent
  6. 1.5. Stochastic Gradient Descent — scikit-learn 0.17 documentation

Support Vector Machines

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  1. SKLearn Kernel Approximation
  2. Kernels Part 1: What is an RBF Kernel? Really?
  3. SKLearn SVMs
  4. Preprocessing Data in SKLearn
  5. How to tune SVM parameters
  6. Lecture on SVM
  7. 5 more

Tutorials

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  1. Unsupervised Feature Learning and Deep Learning Tutorial

Deep Learning Frameworks

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  1. Keras Documentation
  2. Theano 0.7 documentation
  3. Deep Learning Framework

Natural Language Processing

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  1. Word2vec-From Data to Decisions
  2. Text-Analysis-with-NLTK-Cheatsheet.pdf
  3. Finding the K in K-Means Clustering
  4. Cosine Similarity for Vector Space Models
  5. Text Feature Extraction tfidf
  6. Amazing clustering walkthrough
  7. 8 more

NLP Libraries

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  1. Count vectorizers

Neural Network and Deep Learning

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  1. Gradient Based Learning
  2. Neural networks and deep learning
  3. Feed Forward Multilayer Perceptron (newff)
  4. colah's blog - deep learning
  5. Neural networks
  6. Neural Networks Demystified [Part 1: Data and Architecture]
  7. 2 more

Pybrain and pylearn2

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  1. Pylearn2 in practice
  2. Pylearn2
  3. PyBrain

Restricted Boltzmann Machines

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  1. Restricted Boltzmann Machine features for digit classification
  2. Restricted Boltzmann Machines (RBM)
  3. more RBM

Cool Shit

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  1. Long-term storage for Google Code Project Hosting.
  2. BigML is Machine Learning for everyone

MISC Machine Learning

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  1. confusion matrix
  2. fmfn/UnbalancedDataset
  3. 8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset
  4. Useful Machine Learning
  5. SVM Tutorial
  6. SVMs SVC
  7. 1 more

F-Testing

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  1. F Statistic: Definition and How to find it
  2. What Is the F-test of Overall Significance in Regression Analysis?

Gap Statistics

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  1. Clustering With K-Means in Python
  2. Finding the K in K-Means Clustering
  3. gap.pdf
  4. sklearn return inertias
  5. sklearn _assign_labels_csr

Singular Value Decomposition and Nonnegative Matrix Factorization

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  1. Matrix Factorization
  2. What is the difference between non-negative matrix factorization and singular value dec...
  3. commonsense/divisi2
  4. scikit-learn NMF
  5. Non-negative matrix factorization
  6. Singular value decomposition

General Statistics

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  1. Deviance (statistics)
  2. hypothesis_testing.py
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