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Machine Learning Automation with TPOT

You're reading from Machine Learning Automation with TPOT Build, validate, and deploy fully automated machine learning models with Python

Product type Paperback

Published in May 2021

Publisher Packt

ISBN-13 9781800567887

Length 270 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1):

Dario Radečić

View More author details

Table of Contents (14) Chapters

Preface

1. Section 1: Introducing Machine Learning and the Idea of Automation

2. Chapter 1: Machine Learning and the Idea of Automation FREE CHAPTER

3. Section 2: TPOT – Practical Classification and Regression

4. Chapter 2: Deep Dive into TPOT

5. Chapter 3: Exploring Regression with TPOT

6. Chapter 4: Exploring Classification with TPOT

7. Chapter 5: Parallel Training with TPOT and Dask

8. Section 3: Advanced Examples and Neural Networks in TPOT

9. Chapter 6: Getting Started with Deep Learning: Crash Course in Neural Networks

10. Chapter 7: Neural Network Classifier with TPOT

11. Chapter 8: TPOT Model Deployment

12. Chapter 9: Using the Deployed TPOT Model in Production

13. Other Books You May Enjoy

Technical requirements

You don't need any prior hands-on experience with deep learning and neural networks. A knowledge of some basics concepts and terminology is a must, however. If you're entirely new to the subject, please revisit Chapter 6, Getting Started with Deep Learning: Crash Course in Neural Networks.

You can download the source code and dataset for this chapter here: https://github.com/PacktPublishing/Machine-Learning-Automation-with-TPOT/tree/main/Chapter07.

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Authors (1)

Radečić

Radečić

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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