Preface
Page: i-iii (3)
Author: Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar*
DOI: 10.2174/9789815136982123010001
Introduction to Machine Learning
Page: 1-5 (5)
Author: Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar*
DOI: 10.2174/9789815136982123010002
PDF Price: $15
Abstract
Machine learning, a rapidly growing subfield of computer science, has had a
significant impact on many industries and our lives. This chapter discusses the brief history of
machine learning, its widespread adoption as a de facto feature, and fundamental concepts such
as supervised and unsupervised learning, regression and classification, and underfitting and
overfitting. We also emphasize the importance of understanding machine learning through
numerical examples, which can bridge the gap between abstract mathematical theories and
practical applications of machine learning algorithms. By developing a strong foundation in
machine learning, readers/students can harness its potential to address challenges and
opportunities across diverse sectors.
Linear Regression
Page: 6-27 (22)
Author: Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar*
DOI: 10.2174/9789815136982123010003
PDF Price: $15
Abstract
In this chapter, we delve into linear regression, a fundamental machine learning
algorithm for predicting numerical values. While maintaining a concise overview of the
mathematical theories, we prioritize an accessible approach by focusing on a concrete
numerical example with a small dataset for predicting house sale prices. Through a step-bystep walkthrough, we illustrate the inner workings of linear regression and demonstrate its
practical implementation. Additionally, we offer sample codes and a comparison with the linear
regression model from scikit-learn to reinforce understanding. Upon completing this chapter,
readers will gain a comprehensive understanding of linear regression's inner workings and its
relationship to algorithm implementation and performance, and be better prepared to apply it
to real-world projects.
Regularization
Page: 28-70 (43)
Author: Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar*
DOI: 10.2174/9789815136982123010004
PDF Price: $15
Abstract
This chapter delves into L1 and L2 regularization techniques within the context of
linear regression, focusing on minimizing overfitting risks while maintaining a concise
presentation of mathematical theories. We explore these techniques through a concrete
numerical example with a small dataset for predicting house sale prices, providing a step-by-step walkthrough of the process. To further enhance comprehension, we supply sample codes
and draw comparisons with the Lasso and Ridge models implemented in the scikit-learn library.
By the end of this chapter, readers will acquire a well-rounded understanding of L1 and L2
regularization in the context of linear regression, their implications on model implementation
and performance, and be equipped with the knowledge to apply these methods in practical use.
Logistic Regression
Page: 71-96 (26)
Author: Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar*
DOI: 10.2174/9789815136982123010005
PDF Price: $15
Abstract
This chapter delves into logistic regression, a widely used machine learning
algorithm for classification tasks, with a focus on maintaining accessibility by minimizing
abstract mathematical concepts. We present a concrete numerical example employing a small
dataset to predict the ease of selling houses in the property market, guiding readers through
each step of the process. Additionally, we supply sample codes and draw comparisons with the
logistic regression model available in the scikit-learn library. Upon completion of this chapter,
readers will have gained a comprehensive understanding of the inner workings of logistic
regression, its relationship to algorithm implementation and performance, and the knowledge
necessary to apply it to practical applications.
Decision Tree
Page: 97-115 (19)
Author: Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar*
DOI: 10.2174/9789815136982123010006
PDF Price: $15
Abstract
In this chapter, we explore the concept of decision trees, prioritizing accessibility by
minimizing abstract mathematical theories. We examine a concrete numerical example using a
small dataset to predict the suitability of playing tennis based on weather conditions, guiding
readers through the process step-by-step. Moreover, we provide sample codes and compare
them with the decision tree classification model found in the scikit-learn library. Upon
completing this chapter, readers will have gained a comprehensive understanding of the inner
workings of decision tree machine learning, the relationship between the underlying principles,
and the implementation and performance of the algorithm, preparing them to apply their
knowledge to practical scenarios.
Gradient Boosting
Page: 116-159 (44)
Author: Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar*
DOI: 10.2174/9789815136982123010007
PDF Price: $15
Abstract
In this chapter, we explore gradient boosting, a powerful ensemble machine learning
method, for both regression and classification tasks. With a focus on accessibility, we minimize
abstract mathematical theories and instead emphasize two concrete numerical examples with
small datasets related to predicting house sale prices and ease of selling houses in the property
market. By providing a step-by-step walkthrough, we illuminate the inner workings of gradient
boosting and offer sample codes and comparisons to the gradient boosting models available in
the scikit-learn library. Upon completing this chapter, readers will possess a comprehensive
understanding of gradient boosting's mechanics, its connection to the implementation and
performance of the algorithm, and be well-prepared to apply it in real-world projects.
Support Vector Machine
Page: 160-193 (34)
Author: Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar*
DOI: 10.2174/9789815136982123010008
PDF Price: $15
Abstract
In this chapter, we investigate Support Vector Machines (SVM) for both linearly
separable and linearly non-separable cases, emphasizing accessibility by minimizing abstract
mathematical theories. We present concrete numerical examples with small datasets and
provide a step-by-step walkthrough, illustrating the inner workings of SVM. Additionally, we
offer sample codes and comparisons with the SVM model available in the scikit-learn library.
Upon completing this chapter, readers will gain a comprehensive understanding of SVM's
mechanics, and its connection to the implementation and performance of the algorithm, and be
well-prepared to apply it in their practical applications.
K-means Clustering
Page: 194-211 (18)
Author: Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar*
DOI: 10.2174/9789815136982123010009
PDF Price: $15
Abstract
In this chapter, we explore the K-means clustering algorithm, emphasizing an
accessible approach by minimizing abstract mathematical theories. We present a concrete
numerical example with a small dataset to illustrate how clusters can be formed using the Kmeans clustering algorithm. Additionally, we provide sample codes and comparisons with the
K-means model available in the scikit-learn library. Upon completing this chapter, readers will
gain a comprehensive understanding of the mechanics behind K-means clustering, and its
connection to the implementation and performance of the algorithm, and be well-prepared to
apply it in practical use.
Subject Index
Page: 212-215 (4)
Author: Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar*
DOI: 10.2174/9789815136982123010010
Introduction
Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. Key features - Provides a concise introduction to numerical concepts in machine learning in simple terms - Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables - Focuses on numerical examples while using small datasets for easy learning - Includes simple Python codes - Includes bibliographic references for advanced reading The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses.