Machine Learning: Theory and Implementation with Python
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Machine Learning: Theory and Implementation with Python

About the Book

Many of us have had started or are going to start our machine learning journey with just two lines of code using method. This is incredible, thanks to latest machine learning libraries; with little efforts now we can apply machine learning algorithms on real world datasets. However, there is one problem. This abstracts away fundamental concepts of machine learning algorithms. Not all algorithms are suitable for all types of datasets and problems. Solving machine learning problems eventually require in-depth knowledge of machine learning algorithms.

There are many machine learning books that discuss how to use machine learning libraries and apply on the datasets. Numerous books are also available that cover advanced mathematical concepts of machine learning algorithms. However, there are few resources that cover "how machine learning algorithms work" from theory and implementation angle. That's why we are calling this book as "Machine Learning: Theory and Implementation with Python".

This book is for current and aspiring machine learning practitioners, who are interested in learning "machine learning from scratch". Theoretical concepts and implementation of machine learning algorithms with practical code examples is the basis of this book. Conscious effort is made to keep this book as concise as possible, and to the point that all machine learning practitioners need to know.

About the Author

Tanvir Islam
Tanvir Islam

Dr. Tanvir Islam is presently a senior data scientist at Okta, specialized in machine learning, algorithms, optimization, statistics, and big data technologies. Previously, he held research scientist positions at NASA JPL, Caltech, and NOAA. He holds a PhD in Engineering (Machine Learning and Sensing) from the University of Bristol. He has numerous publications in machine learning, deep learning, artificial intelligence, rover autonomy, optimization techniques, and data-driven systems.

Table of Contents


1. Introduction

2. Linear Algebra

3. Simple Linear Regression

4. Multiple Linear Regression

5. Logistic Regression: Binary Classification

6. Logistic Regression: Multi-class Classification

7. Regularization

8. K-nearest Neighbors

9. Neural Networks

10. Support Vector Machines

11. Decision Trees

12. Ensemble Learning

13. K-means Clustering

14. Principal Component Analysis

15. Gaussian Anomaly Detection

16. Machine Learning in Practice

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