Statistics for Machine Learning

Book Image

Statistics for Machine Learning

By : Pratap Dangeti

Book Image

Statistics for Machine Learning

By: Pratap Dangeti

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 Author

About the Reviewer

About the Reviewer

www.PacktPub.com

www.PacktPub.com

Customer Feedback

Customer Feedback

Preface

Free Chapter

Journey from Statistics to Machine Learning

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

Parallelism of Statistics and 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

Logistic Regression Versus Random Forest

Logistic Regression Versus Random Forest

Maximum likelihood estimation

Logistic regression – introduction and advantages

Variable importance plot

Comparison of logistic regression with random forest

Tree-Based Machine Learning Models

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

K-Nearest Neighbors and Naive Bayes

K-Nearest Neighbors and Naive Bayes

K-nearest neighbors

KNN classifier with breast cancer Wisconsin data example

Tuning of k-value in KNN classifier

Probability fundamentals

Understanding Bayes theorem with conditional probability

Naive Bayes classification

Laplace estimator

Naive Bayes SMS spam classification example

Support Vector Machines and Neural Networks

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

Recommendation Engines

Recommendation Engines

Content-based filtering

Collaborative filtering

Evaluation of recommendation engine model

Unsupervised Learning

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

Reinforcement 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 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

Further reading

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Characteristics of reinforcement learning

The feedback of a reward signal is not instantaneous. It is delayed by many timesteps
Sequential decision making is needed to reach a goal, so time plays an important role in reinforcement problems (no IID assumption of the data holds good here)
The agent's action affects the subsequent data it receives

In reinforcement learning, a little bit of supervision is needed, but much less supervision compared to supervised learning.

The following are a few actual live examples of reinforcement learning problems:

Autonomous helicopter: The objective of autonomous helicopter is to change its roll, pitch and yaw to control its position by controlling the joystick, pedals, and so on. Sensors send inputs 10 times a second which provide an accurate estimate of position and orientation of the helicopter. The helicopter's job is to receive this input and to control the stick to move accordingly. It is very hard to provide information on what the helicopter needs to...