دانلود کتاب Machine Learning Automation with TPOT

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Cover Title Page Copyright and Credits Contributors Table of Contents Preface 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 Section 3: Advanced Examples and Neural Networks in TPOT 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 Chapter 9: Using the Deployed TPOT Model in Production Technical requirements Making predictions in a notebook environment Developing a simple GUI web application Making predictions in a GUI environment Summary Q&A Why subscribe? About Packt Other Books You May Enjoy Index