Name: Building Machine Learning Systems Using Python
Price: 19.95 USD
ISBN: 9789389423617

BPB Online LLP

Deepti Chopra

Building Machine Learning Systems Using Python

US$ 19.95

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Description

Contents

Reviews

Explore Machine Learning Techniques, Different Predictive Models, and its Applications

Key Features
● Extensive coverage of real examples on implementation and working of ML models.
● Includes different strategies used in Machine Learning by leading data scientists.
● Focuses on Machine Learning concepts and their evolution to algorithms.

Description
This book covers basic concepts of Machine Learning, various learning paradigms, different architectures and algorithms used in these paradigms.

You will learn the power of ML models by exploring different predictive modeling techniques such as Regression, Clustering, and Classification. You will also get hands-on experience on methods and techniques such as Overfitting, Underfitting, Random Forest, Decision Trees, PCA, and Support Vector Machines. In this book real life examples with fully working of Python implementations are discussed in detail.

At the end of the book you will learn about the unsupervised learning covering Hierarchical Clustering, K-means Clustering, Dimensionality Reduction, Anomaly detection, Principal Component Analysis.

What you will learn
● Learn to perform data engineering and analysis.
● Build prototype ML models and production ML models from scratch.
● Develop strong proficiency in using scikit-learn and Python.
● Get hands-on experience with Random Forest, Logistic Regression, SVM, PCA, and Neural Networks.

Who this book is for
This book is meant for beginners who want to gain knowledge about Machine Learning in detail. This book can also be used by Machine Learning users for a quick reference for fundamentals in Machine Learning. Readers should have basic knowledge of Python and Scikit-Learn before reading the book.

Table of Contents
1. Introduction to Machine Learning
2. Linear Regression
3. Classification Using Logistic Regression
4. Overfitting and Regularization
5. Feasibility of Learning
6. Support Vector Machine
7. Neural Network
8. Decision Trees
9. Unsupervised Learning
10. Theory of Generalization
11. Bias and Fairness in ML

About the Authors
Dr Deepti Chopra is working as an Assistant Professor (IT) at Lal Bahadur Shastri Institute of Management, Delhi. She has around 7 years of teaching experience. Her areas of interest include Natural Language Processing, Computational Linguistics, and Artificial Intelligence. She is the author of three books and has written several research papers in various international conferences and journals.

Language

English

ISBN

9789389423617

artificial intelligence

reinforcement learning

supervised learning

support vector machines

unsupervised learning

Cover Page

Title Page

Dedication Page

About the Author

About the Reviewer

Acknowledgement

Preface

Errata

Table of Contents

1. Introduction

Structure

Objectives

History of machine learning

Classification of machine learning

Challenges faced in adopting machine learning

Applications

Conclusion

Questions

2. Linear Regression

Structure

Objectives

Linear regression in one variable

Linear regression in multiple variables

Gradient descent

Polynomial regression

Conclusion

Questions

3. Classification Using Logistic Regression

Introduction

Structure

Objectives

Binary classification

Logistic regression

Multiclass classification

Conclusion

Questions

4. Overfitting and Regularization

Structure

Objectives

Overfitting and regularization in linear regression

Overfitting and regularization in logistic regression

Conclusion

Questions

5. Feasibility of Learning

Introduction

Structure

Objectives

Feasibility of learning an unknown target function

In-sample error and out-of-sample error

Conclusion

Questions

6. Support Vector Machine

Introduction

Structure

Objectives

Margin and Large Margin methods

Kernel methods

Conclusion

Questions

7. Neural Network

Introduction

Structure

Objectives

Early models

Perceptron learning

Back propagation

Stochastic Gradient Descent

Conclusion

Questions

8. Decision Trees

Introduction

Structure

Objectives

Decision trees

Regression trees

Stopping criterion and pruning loss functions in decision trees

Categorical attributes, multiway splits, and missing values in decision trees

Instability in decision trees

Conclusion

Questions

9. Unsupervised Learning

Introduction

Structure

Objectives

Clustering

K-means clustering

Hierarchical clustering

Principal Component Analysis (PCA)

Conclusion

Questions

10. Theory of Generalization

Introduction

Structure

Objectives

Training versus testing

Bounding the testing error

VC dimension

Conclusion

Questions

11. Bias and Fairness in Machine Learning

Introduction

Structure

Objectives

Introduction

How to detect bias?

How to fix biases or achieve fairness in ML?

Conclusion

Questions

Index

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