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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

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Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781800567887
Length 270 pages
Edition 1st Edition
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Author (1):
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Dario Radečić Dario Radečić
Author Profile Icon Dario Radečić
Dario Radečić
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Toc

Table of Contents (14) Chapters Close

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

Introducing TPOT

TPOT, or Tree-based Pipeline Optimization Tool, is an open source library for performing machine learning in an automated fashion with the Python programming language. Below the surface, it uses the well-known scikit-learn machine learning library to perform data preparation, transformation, and machine learning. It also uses GP procedures to discover the best-performing pipeline for a given dataset. The concept of GP is covered in later sections.

As a rule of thumb, you should use TPOT every time you need an automated machine learning pipeline. Data science is a broad field, and libraries such as TPOT enable you to spend much more time on data gathering and cleaning, as everything else is done automatically.

The following figure shows what a typical machine learning pipeline looks like:

Figure 2.1 – Example machine learning pipeline

The preceding figure shows which parts of a machine learning process can and can't be automated...

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