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

Model Validation and Testing

Nowadays, it is easy for almost anybody to start working in a machine-learning project with all the information available online. However, choosing the right algorithm for your data is a challenge when there are many alternatives available. Due to this, the right algorithm is chosen by a process of trial and error, where the different alternatives are tested.

Moreover, the decision process to arrive at a good model covers not only the selection of the algorithm but also the tuning of its hyperparameters. To do this, a conventional approach is to divide the data into three parts, training, validation, and testing sets, which will be explained further now.

Data Partition

Data partition is a process involving the division of the dataset into three subsets so that each set can be used for a different purpose. This way, the development of a model is not affected by the introduction of bias. The following is an explanation of each subset:

  • Training set: As the name suggests...