Statistics/MachineLearning

Cool blogs

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

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

Generalized Linear Model

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

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

OLS

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

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

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

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]

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

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

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 decomposition?
  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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