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Table Of Contents

Hands-On Automated Machine Learning

By : Das, Mert Cakmak

Buy this Book

Hands-On Automated Machine Learning

By: Das, Mert Cakmak

Buy this Book

Overview of this book

AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you’ll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you’ll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Introduction to AutoML

Scope of machine learning

What is AutoML?

Why use AutoML and how does it help?

When do you automate ML?

What will you learn?

Overview of AutoML libraries

Summary

Introduction to Machine Learning Using Python

Technical requirements

Machine learning

Linear regression

Important evaluation metrics – regression algorithms

Logistic regression

Important evaluation metrics – classification algorithms

Decision trees

Support Vector Machines

k-Nearest Neighbors

Ensemble methods

Comparing the results of classifiers

Cross-validation

Clustering

Summary

Data Preprocessing

Technical requirements

Data transformation

Feature selection

Feature generation

Summary

Automated Algorithm Selection

Technical requirements

Computational complexity

Differences in training and scoring time

Linearity versus non-linearity

Necessary feature transformations

Supervised ML

Unsupervised AutoML

Summary

Hyperparameter Optimization

Technical requirements

Hyperparameters

Warm start

Bayesian-based hyperparameter tuning

An example system

Summary

Creating AutoML Pipelines

Technical requirements

An introduction to machine learning pipelines

A simple pipeline

FunctionTransformer

A complex pipeline

Summary

Dive into Deep Learning

Technical requirements

Overview of neural networks

A feed-forward neural network using Keras

Autoencoders

Convolutional Neural Networks

Summary

Critical Aspects of ML and Data Science Projects

Machine learning as a search

Trade-offs in machine learning

Engagement model for a typical data science project

The phases of an engagement model

Summary

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Cross-validation

Cross-validation is a way to evaluate the accuracy of a model on a dataset that was not used for training, that is, a sample of data that is unknown to trained models. This ensures generalization of a model on independent datasets when deployed in a production environment. One of the methods is dividing the dataset into two sets—train and test sets. We demonstrated this method in our previous examples.

Another popular and more robust method is a k-fold cross-validation approach, where a dataset is partitioned into k subsamples of equal sizes. Where k is a non-zero positive integer. During the training phase, k-1 samples are used to train the model and the remaining one sample is used to test the model. This process is repeated for k times with one of the k samples used exactly once to test the model. The evaluation results are then averaged or combined...