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)

Saving and Loading a Trained Model

Although the process of manipulating a dataset and training the right model is crucial for developing a machine learning project, the work does not end there. Knowing how to save a trained model is key as this will allow you to save the hyperparameters, as well as the values for the weights and biases of your final model, so that it remains unchanged when it is run again.

Moreover, after the model has been saved to a file, it is also important to know how to load the saved model in order to use it to make predictions on new data. By saving and loading a model, we allow for the model to be reused at any moment and through many different means.

Saving a Model

The process of saving a model is also called serialization, and it has become increasingly important due to the popularity of neural networks that use many parameters (weights and biases) that are randomly initialized every time the model is trained, as well as due to the introduction...