Authoring Machine Learning Models from Scratch: A Step-by-Step Guide to Understanding Machine Learning Algorithms in Python

A complete guide to learning the details of machine learning algorithms by implementing them from scratch in Python. You will discover how to load data, evaluate models, and implement a suite of top machine learning algorithms using step-by-step tutorials. Machine learning algorithms do have a lot of math and theory under the covers, but you do not need to know why algorithms work to be able to implement them and apply them to achieve real and valuable results. In this course, you will learn how to load from CSV files and prepare data for modeling; how to select algorithm evaluation metrics and resampling techniques for a test harness; how to develop a baseline expectation of performance for a given problem; how to implement and apply a suite of linear machine learning algorithms; how to implement and apply a suite of advanced nonlinear machine learning algorithms; how to implement and apply ensemble machine learning algorithms to improve performance. This course will be an invaluable guide to understanding real-world machine learning models and help you understand the code behind math. By the end of this course, you will gain insight into real-world machine learning models and learn how to code the functions of the most used tools in machine learning. The complete code bundle for this course is available at https://github.com/PacktPublishing/Authoring-Machine-Learning-Models-from-Scratch

This course is for developers, machine learning engineers, and data scientists who want to learn how to get the most out of Keras. You do not need to be a machine learning expert, but it would be helpful if you knew how to navigate a small machine learning problem using SciKit-Learn. Additionally, you should have a solid background in Python.