Machine Learning: Theory and Implementation with Python
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 model.fit() 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
Table of Contents
Chapters
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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