Statistics for Machine Learning by Dangeti, Pratap -- Read -- Imperial Library of Trantor

Index

Title Page Copyright

Statistics for Machine Learning

Credits About the Author About the Reviewer www.PacktPub.com

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Customer Feedback Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Downloading the color images of this book Errata Piracy Questions

Journey from Statistics to Machine Learning

Statistical terminology for model building and validation

Machine learning Major differences between statistical modeling and machine learning Steps in machine learning model development and deployment Statistical fundamentals and terminology for model building and validation Bias versus variance trade-off Train and test data

Machine learning terminology for model building and validation

Linear regression versus gradient descent Machine learning losses When to stop tuning machine learning models Train, validation, and test data Cross-validation Grid search

Machine learning model overview Summary

Parallelism of Statistics and Machine Learning

Comparison between regression and machine learning models Compensating factors in machine learning models

Assumptions of linear regression Steps applied in linear regression modeling Example of simple linear regression from first principles Example of simple linear regression using the wine quality data Example of multilinear regression - step-by-step methodology of model building

Backward and forward selection

Machine learning models - ridge and lasso regression

Example of ridge regression machine learning Example of lasso regression machine learning model Regularization parameters in linear regression and ridge/lasso regression

Summary

Logistic Regression Versus Random Forest

Maximum likelihood estimation Logistic regression – introduction and advantages

Terminology involved in logistic regression Applying steps in logistic regression modeling Example of logistic regression using German credit data

Random forest

Example of random forest using German credit data

Grid search on random forest

Variable importance plot Comparison of logistic regression with random forest Summary

Tree-Based Machine Learning Models

Introducing decision tree classifiers

Terminology used in decision trees Decision tree working methodology from first principles

Comparison between logistic regression and decision trees Comparison of error components across various styles of models Remedial actions to push the model towards the ideal region HR attrition data example Decision tree classifier Tuning class weights in decision tree classifier Bagging classifier Random forest classifier Random forest classifier - grid search AdaBoost classifier Gradient boosting classifier Comparison between AdaBoosting versus gradient boosting Extreme gradient boosting - XGBoost classifier Ensemble of ensembles - model stacking Ensemble of ensembles with different types of classifiers Ensemble of ensembles with bootstrap samples using a single type of classifier Summary

K-Nearest Neighbors and Naive Bayes

K-nearest neighbors

KNN voter example Curse of dimensionality

Curse of dimensionality with 1D, 2D, and 3D example

KNN classifier with breast cancer Wisconsin data example Tuning of k-value in KNN classifier Naive Bayes Probability fundamentals

Joint probability

Understanding Bayes theorem with conditional probability Naive Bayes classification Laplace estimator Naive Bayes SMS spam classification example Summary

Support Vector Machines and Neural Networks

Support vector machines working principles

Maximum margin classifier Support vector classifier Support vector machines

Kernel functions SVM multilabel classifier with letter recognition data example

Maximum margin classifier - linear kernel Polynomial kernel RBF kernel

Artificial neural networks - ANN Activation functions Forward propagation and backpropagation Optimization of neural networks

Stochastic gradient descent - SGD Momentum Nesterov accelerated gradient - NAG Adagrad Adadelta RMSprop Adaptive moment estimation - Adam Limited-memory broyden-fletcher-goldfarb-shanno - L-BFGS optimization algorithm

Dropout in neural networks ANN classifier applied on handwritten digits using scikit-learn Introduction to deep learning

Solving methodology Deep learning software Deep neural network classifier applied on handwritten digits using Keras

Summary

Recommendation Engines

Content-based filtering

Cosine similarity

Collaborative filtering

Advantages of collaborative filtering over content-based filtering Matrix factorization using the alternating least squares algorithm for collaborative filtering

Evaluation of recommendation engine model

Hyperparameter selection in recommendation engines using grid search Recommendation engine application on movie lens data

User-user similarity matrix Movie-movie similarity matrix Collaborative filtering using ALS Grid search on collaborative filtering

Summary

Unsupervised Learning

K-means clustering

K-means working methodology from first principles Optimal number of clusters and cluster evaluation

The elbow method

K-means clustering with the iris data example

Principal component analysis - PCA

PCA working methodology from first principles PCA applied on handwritten digits using scikit-learn

Singular value decomposition - SVD

SVD applied on handwritten digits using scikit-learn

Deep auto encoders Model building technique using encoder-decoder architecture Deep auto encoders applied on handwritten digits using Keras Summary

Reinforcement Learning

Introduction to reinforcement learning Comparing supervised, unsupervised, and reinforcement learning in detail Characteristics of reinforcement learning Reinforcement learning basics

Category 1 - value based Category 2 - policy based Category 3 - actor-critic Category 4 - model-free Category 5 - model-based Fundamental categories in sequential decision making

Markov decision processes and Bellman equations Dynamic programming

Algorithms to compute optimal policy using dynamic programming

Grid world example using value and policy iteration algorithms with basic Python Monte Carlo methods

Comparison between dynamic programming and Monte Carlo methods Key advantages of MC over DP methods Monte Carlo prediction The suitability of Monte Carlo prediction on grid-world problems Modeling Blackjack example of Monte Carlo methods using Python

Temporal difference learning

Comparison between Monte Carlo methods and temporal difference learning TD prediction Driving office example for TD learning

SARSA on-policy TD control Q-learning - off-policy TD control Cliff walking example of on-policy and off-policy of TD control Applications of reinforcement learning with integration of machine learning and deep learning

Automotive vehicle control - self-driving cars Google DeepMind's AlphaGo Robo soccer