Book description
Build Machine Learning models with a sound statistical understanding.
About This Book
 Learn about the statistics behind powerful predictive models with pvalue, ANOVA, and F statistics.
 Implement statistical computations programmatically for supervised and unsupervised learning through Kmeans clustering.
 Master the statistical aspect of Machine Learning with the help of this examplerich guide to R and Python.
Who This Book Is For
This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.
What You Will Learn
 Understand the Statistical and Machine Learning fundamentals necessary to build models
 Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems
 Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the morethanadequate R and Python packages
 Analyze the results and tune the model appropriately to your own predictive goals
 Understand the concepts of required statistics for Machine Learning
 Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models
 Learn reinforcement learning and its application in the field of artificial intelligence domain
In Detail
Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the realworld examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.
By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.
Style and approach
This practical, stepbystep guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.
Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.
Publisher resources
Table of contents
 Preface
 Journey from Statistics to Machine Learning

Parallelism of Statistics and Machine Learning
 Comparison between regression and machine learning models
 Compensating factors in machine learning models
 Machine learning models  ridge and lasso regression
 Summary
 Logistic Regression Versus Random Forest

TreeBased Machine Learning Models
 Introducing decision tree classifiers
 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

KNearest Neighbors and Naive Bayes
 Knearest neighbors
 KNN classifier with breast cancer Wisconsin data example
 Tuning of kvalue in KNN classifier
 Naive Bayes
 Probability fundamentals
 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
 Kernel functions
 SVM multilabel classifier with letter recognition data example
 Artificial neural networks  ANN
 Activation functions
 Forward propagation and backpropagation
 Optimization of neural networks
 Dropout in neural networks
 ANN classifier applied on handwritten digits using scikitlearn
 Introduction to deep learning
 Summary
 Recommendation Engines
 Unsupervised Learning

Reinforcement Learning
 Introduction to reinforcement learning
 Comparing supervised, unsupervised, and reinforcement learning in detail
 Characteristics of reinforcement learning
 Reinforcement learning basics
 Markov decision processes and Bellman equations
 Dynamic programming
 Grid world example using value and policy iteration algorithms with basic Python
 Monte Carlo methods
 Temporal difference learning
 SARSA onpolicy TD control
 Qlearning  offpolicy TD control
 Cliff walking example of onpolicy and offpolicy of TD control
 Applications of reinforcement learning with integration of machine learning and deep learning
 Further reading
 Summary
Product information
 Title: Statistics for Machine Learning
 Author(s):
 Release date: July 2017
 Publisher(s): Packt Publishing
 ISBN: 9781788295758
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