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Book Overview & Buying
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Table Of Contents
Machine Learning Fundamentals
By :
Machine Learning Fundamentals
By:
Overview of this book
As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem.
The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters.
By the end of this book, you will have gain all the skills required to start programming machine learning algorithms.
Table of Contents (8 chapters)
Preface
Free Chapter
Introduction to Scikit-Learn
Unsupervised Learning: Real-Life Applications
Supervised Learning: Key Steps
Supervised Learning Algorithms: Predict Annual Income
Artificial Neural Networks: Predict Annual Income
Building Your Own Program
Appendix
