Authoring Machine Learning Models from Scratch

Description

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

What You Will Learn

Develop a baseline expectation of performance for a given problem
Learn to code the functions of the most used tools in machine learning
Gain insight into who real-world machine learning models are written
Gain a deep appreciation for how the algorithm works
Implement and apply a suite of linear machine learning algorithms
Implement and apply a suite of advanced non-linear ML algorithms

Audience

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.

Approach

This course is a hands-on guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to the mechanics of machine learning models in Python. To get the most out of the course, I would recommend working through all the examples in each tutorial. If you watch this course like a movie, you will get little out of it.

Key Features

Know how top machine learning algorithms work internally * Learn to configure machine learning algorithms to get the most out of them * Understand the myriad of micro-decisions that a machine learning library has hidden from you in practice

Github Repo

https://github.com/PacktPublishing/Authoring-Machine-Learning-Models-from-Scratch

About the Author

Mike West

Mike West is the founder of LogikBot. He has worked with databases for over two decades. He has worked for or consulted with over 50 different companies as a full-time employee or consultant. These were Fortune 500 as well as several small to mid-size companies. Some include Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light, and Northrup Grumman. Over the last five years, Mike has transitioned to the exciting world of applied machine learning. He is excited to show you what he has learned and help you move into one of the single-most important fields in this space.

Course Outline

1. Introduction

2. Data Preparation

3. Linear Algorithms

4. Non-Linear Regression