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Machine Learning Automation with TPOT
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Machine Learning Automation with TPOT
by
Machine Learning Automation with TPOT
Machine Learning Automation with TPOT
Contributors
About the author
About the reviewer
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Section 1: Introducing Machine Learning and the Idea of Automation
Chapter 1: Machine Learning and the Idea of Automation
Technical requirements
Reviewing the history of machine learning
What is machine learning?
In which sectors are the companies using machine learning?
Supervised learning
Reviewing automation
What is automation?
Why is automation needed?
Are machine learning and automation the same thing?
Applying automation to machine learning
What are we trying to automate?
The problem of too many parameters
What is AutoML?
Automation options
PyCaret
ObviouslyAI
TPOT
Summary
Q&A
Further reading
Section 2: TPOT – Practical Classification and Regression
Chapter 2: Deep Dive into TPOT
Technical requirements
Introducing TPOT
A brief overview of genetic programming
TPOT limitations
Types of problems TPOT can solve
How TPOT handles regression tasks
How TPOT handles classification tasks
Installing TPOT and setting up the environment
Installing and configuring TPOT with standalone Python installation
Installing and configuring TPOT through Anaconda
Summary
Q&A
Further reading
Chapter 3: Exploring Regression with TPOT
Technical requirements
Applying automated regression modeling to the fish market dataset
Applying automated regression modeling to the insurance dataset
Applying automated regression modeling to the vehicle dataset
Summary
Q&A
Chapter 4: Exploring Classification with TPOT
Technical requirements
Applying automated classification models to the iris dataset
Applying automated classification modeling to the titanic dataset
Summary
Q&A
Chapter 5: Parallel Training with TPOT and Dask
Technical requirements
Introduction to parallelism in Python
Introduction to the Dask library
Training machine learning models with TPOT and Dask
Summary
Q&A
Chapter 6: Getting Started with Deep Learning: Crash Course in Neural Networks
Technical requirements
Overview of deep learning
Introducing artificial neural networks
Theory of a single neuron
Coding a single neuron
Theory of a single layer
Coding a single layer
Activation functions
Using neural networks to classify handwritten digits
Neural networks in regression versus classification
Summary
Q&A
Chapter 7: Neural Network Classifier with TPOT
Technical requirements
Exploring the dataset
Exploring options for training neural network classifiers
Training a neural network classifier
Summary
Questions
Chapter 8: TPOT Model Deployment
Technical requirements
Why do we need model deployment?
Introducing Flask and Flask-RESTful
Best practices for deploying automated models
Deploying machine learning models to localhost
Deploying machine learning models to the cloud
Summary
Question
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