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

Chapter 3. Supervised Learning: Key Steps


Learning Objectives

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

This chapter explains the methodology to approach a machine learning classification problem.

  • Explain the difference between training, validation, and testing sets

  • Perform data partitioning for split or cross validation

  • Describe the different metrics to evaluate performance

  • Choose the performance metric that fits the purpose of the study

  • Perform error analysis