Statistics for Machine Learning

Statistics for Machine Learning

By : Pratap Dangeti

Buy this Book

Statistics for Machine Learning

By: Pratap Dangeti

Buy this Book

Overview of this book

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 the complex statistical computations that are required for machine learning. You will gain information on the statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and familiarize yourself with it. You will come across programs for performing tasks such as modeling, parameter fitting, regression, classification, density collection, working with vectors, matrices, and more. By the end of the book, you will have mastered the statistics required for machine learning and will be able to apply your new skills to any sort of industry problem.

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

Free Chapter

Journey from Statistics to Machine Learning

Statistical terminology for model building and validation

Machine learning terminology for model building and validation

Machine learning model overview

Summary

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

Maximum likelihood estimation

Logistic regression – introduction and advantages

Random forest

Variable importance plot

Comparison of logistic regression with random forest

Summary

Tree-Based 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

K-Nearest Neighbors and Naive Bayes

K-nearest neighbors

KNN classifier with breast cancer Wisconsin data example

Tuning of k-value 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 scikit-learn

Introduction to deep learning

Summary

Recommendation Engines

Content-based filtering

Collaborative filtering

Evaluation of recommendation engine model

Unsupervised Learning

K-means clustering

Principal component analysis - PCA

Singular value decomposition - SVD

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

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 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

Probability fundamentals

Before diving into Naive Bayes, it would be good to reiterate the fundamentals. Probability of an event can be estimated from observed data by dividing the number of trails in which an event occurred with total number of trails. For instance, if a bag contains red and blue balls and randomly picked 10 balls one by one with replacement and out of 10, 3 red balls appeared in trails we can say that probability of red is 0.3, p_red = 3/10 = 0.3. Total probability of all possible outcomes must be 100 percent.

If a trail has two outcomes such as email classification either it is spam or ham and both cannot occur simultaneously, these events are considered as mutually exclusive with each other. In addition, if those outcomes cover all possible events, it would be called as exhaustive events. For example, in email classification if P (spam) = 0.1, we will be able to calculate P (ham) = 1- 0.1 = 0.9, these two events are mutually exclusive. In the following Venn diagram, all...

Statistics for Machine Learning

By : Pratap Dangeti

Statistics for Machine Learning

By: Pratap Dangeti

Overview of this book

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