Book Image

The Machine Learning Workshop - Second Edition

By : Hyatt Saleh
Book Image

The Machine Learning Workshop - Second Edition

By: Hyatt Saleh

Overview of this book

Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms. The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one. By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.
Table of Contents (8 chapters)

Interacting with a Trained Model

Once the model has been created and saved, it is time for the last step of building a comprehensive machine learning program: allowing easy interaction with the model. This step not only allows the model to be reused, but also introduces efficiency to the implementation of machine learning solutions by allowing you to perform classifications using just input data.

There are several ways to interact with a model, and the decision that's made between choosing one or the other depends on the nature of the user (the individuals that will be making use of the model on a regular basis). Machine learning projects can be accessed in different ways, some of which require the use of an API, an online or offline program (application), or a website.

Moreover, once the channel is defined based on the preference or expertise of the users, it is important to code the connection between the final user and the model, which could be either a function or a...