Book Image

Machine Learning Fundamentals

By : Hyatt Saleh
Book Image

Machine Learning Fundamentals

By: Hyatt Saleh

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 (9 chapters)
Machine Learning Fundamentals
Preface

Chapter 2. Unsupervised Learning: Real-Life Applications

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Describe how clustering works

  • Import and preprocess a dataset using Pandas and Matplotlib

  • Explain the difference between the three clustering algorithms

  • Solve an unsupervised learning data problem using different algorithms

  • Compare the results of different algorithms to select the one with the best performance

Note

This chapter describes a practical implementation of an unsupervised algorithm to a real-world dataset