This is the code repository for Machine Learning Automation with TPOT, published by Packt.
Build, validate, and deploy fully automated machine learning models with Python
The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods.
This book covers the following exciting features: <First 5 What you'll learn points>
- Get to grips with building automated machine learning models
- Build classification and regression models with impressive accuracy in a short time
- Develop neural network classifiers with AutoML techniques
- Compare AutoML models with traditional, manually developed models on the same datasets
- Create robust, production-ready models
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
output = (inputs[0] * weights[0] +
inputs[1] * weights[1] +
inputs[2] * weights[2] +
inputs[3] * weights[3] +
inputs[4] * weights[4] +
bias)
output
Following is what you need for this book: Data scientists, data analysts, and software developers who are new to machine learning and want to use it in their applications will find this book useful. This book is also for business users looking to automate business tasks with machine learning. Working knowledge of the Python programming language and beginner-level understanding of machine learning are necessary to get started.
With the following software and hardware list you can run all code files present in the book (Chapter 1-15).
| Chapter | Software required | OS required |
|---|---|---|
| 1 | Python 3.6+ | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Dario Radečić is a full-time data scientist at Neos, in Croatia, a part-time data storyteller at Appsilon, in Poland, and a business owner. Dario has a master's degree in data science and years of experience in data science and machine learning, with an emphasis on automated machine learning. He is also a top writer in artificial intelligence on Medium and the owner of a data science blog called Better Data Science.
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Simply click on the link to claim your free PDF.