Machine Learning Quick Reference by Kumar, Rahul -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Machine Learning Quick Reference

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Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Quantifying Learning Algorithms

Statistical models Learning curve

Machine learning Wright's model

Curve fitting

Residual

Statistical modeling – the two cultures of Leo Breiman Training data development data – test data

Size of the training, development, and test set

Bias-variance trade off Regularization

Ridge regression (L2) Least absolute shrinkage and selection operator

Cross-validation and model selection

K-fold cross-validation

Model selection using cross-validation 0.632 rule in bootstrapping Model evaluation

Confusion matrix

Receiver operating characteristic curve

Area under ROC

H-measure Dimensionality reduction Summary

Evaluating Kernel Learning

Introduction to vectors

Magnitude of the vector Dot product

Linear separability Hyperplanes SVM

Support vector

Kernel trick

Kernel Back to Kernel trick

Kernel types

Linear kernel Polynomial kernel Gaussian kernel

SVM example and parameter optimization through grid search Summary

Performance in Ensemble Learning

What is ensemble learning?

Ensemble methods

Bootstrapping

Bagging Decision tree

Tree splitting Parameters of tree splitting

Random forest algorithm

Case study

Boosting

Gradient boosting

Parameters of gradient boosting

Summary

Training Neural Networks

Neural networks

How a neural network works Model initialization Loss function Optimization Computation in neural networks

Calculation of activation for H1

Backward propagation Activation function

Types of activation functions

Network initialization

Backpropagation

Overfitting Prevention of overfitting in NNs Vanishing gradient

Overcoming vanishing gradient

Recurrent neural networks

Limitations of RNNs Use case

Summary

Time Series Analysis

Introduction to time series analysis White noise

Detection of white noise in a series

Random walk Autoregression Autocorrelation Stationarity

Detection of stationarity

AR model Moving average model Autoregressive integrated moving average Optimization of parameters

AR model ARIMA model

Anomaly detection Summary

Natural Language Processing

Text corpus

Sentences Words

Bags of words

TF-IDF

Executing the count vectorizer Executing TF-IDF in Python

Sentiment analysis

Sentiment classification

TF-IDF feature extraction Count vectorizer bag of words feature extraction

Model building count vectorization

Topic modeling

LDA architecture Evaluating the model Visualizing the LDA The Naive Bayes technique in text classification

The Bayes theorem

How the Naive Bayes classifier works

Summary

Temporal and Sequential Pattern Discovery

Association rules Apriori algorithm

Finding association rules

Frequent pattern growth

Frequent pattern tree growth Validation

Importing the library

Summary

Probabilistic Graphical Models

Key concepts Bayes rule Bayes network

Probabilities of nodes CPT Example of the training and test set

Summary

Selected Topics in Deep Learning

Deep neural networks

Why do we need a deep learning model? Deep neural network notation Forward propagation in a deep network Parameters W and b Forward and backward propagation Error computation

Backward propagation Forward propagation equation Backward propagation equation Parameters and hyperparameters Bias initialization

Hyperparameters Use case – digit recognizer

Generative adversarial networks Hinton's Capsule network

The Capsule Network and convolutional neural networks

Summary

Causal Inference

Granger causality F-test

Limitations Use case

Graphical causal models Summary

Advanced Methods

Introduction Kernel PCA Independent component analysis

Preprocessing for ICA Approach

Compressed sensing

Our goal

Self-organizing maps

SOM

Bayesian multiple imputation Summary

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