Hands-on automated machine learning : a beginner's guide to building automated machine learning systems using AutoML and Python / Sibanjan Das, Umit Mert Cakmak.

Format:

Book

Author/Creator:

Das, Sibanjan, author.

Cakmak, Umit Mert, author.

Language:

English

Subjects (All):

Python (Computer program language).

Machine learning.

Physical Description:

1 online resource (282 pages)

Edition:

First edition

Place of Publication:

Birmingham ; Mumbai : Packt Publishing, 2018.

System Details:

text file

Summary:

Automate data and model pipelines for faster machine learning applications About This Book Build automated modules for different machine learning components Understand each component of a machine learning pipeline in depth Learn to use different open source AutoML and feature engineering platforms Who This Book Is For If you're a budding data scientist, data analyst, or Machine Learning enthusiast and are new to the concept of automated machine learning, this book is ideal for you. You'll also find this book useful if you're an ML engineer or data professional interested in developing quick machine learning pipelines for your projects. Prior exposure to Python programming will help you get the best out of this book. What You Will Learn Understand the fundamentals of Automated Machine Learning systems Explore auto-sklearn and MLBox for AutoML tasks Automate your preprocessing methods along with feature transformation Enhance feature selection and generation using the Python stack Assemble individual components of ML into a complete AutoML framework Demystify hyperparameter tuning to optimize your ML models Dive into Machine Learning concepts such as neural networks and autoencoders Understand the information costs and trade-offs associated with AutoML In Detail 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. Style and approach Step by step approach to understand how to automate y...

Contents:

Cover

Copyright and Credits

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: 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?

Core components of AutoML systems

Automated feature preprocessing

Automated algorithm selection

Hyperparameter optimization

Building prototype subsystems for each component

Putting it all together as an end-to-end AutoML system

Overview of AutoML libraries

Featuretools

Auto-sklearn

MLBox

TPOT

Summary

Chapter 2: Introduction to Machine Learning Using Python

Technical requirements

Machine learning

Machine learning process

Supervised learning

Unsupervised learning

Linear regression

What is linear regression?

Working of OLS regression

Assumptions of OLS

Where is linear regression used?

By which method can linear regression be implemented?

Important evaluation metrics - regression algorithms

Logistic regression

What is logistic regression?

Where is logistic regression used?

By which method can logistic regression be implemented?

Important evaluation metrics - classification algorithms

Decision trees

What are decision trees?

Where are decision trees used?

By which method can decision trees be implemented?

Support Vector Machines

What is SVM?

Where is SVM used?

By which method can SVM be implemented?

k-Nearest Neighbors

What is k-Nearest Neighbors?

Where is KNN used?

By which method can KNN be implemented?

Ensemble methods

What are ensemble models?

Bagging

Boosting

Stacking/blending

Comparing the results of classifiers

Cross-validation

Clustering

What is clustering?

Where is clustering used?.

By which method can clustering be implemented?

Hierarchical clustering

Partitioning clustering (KMeans)

Chapter 3: Data Preprocessing

Data transformation

Numerical data transformation

Scaling

Missing values

Outliers

Detecting and treating univariate outliers

Inter-quartile range

Filtering values

Winsorizing

Trimming

Detecting and treating multivariate outliers

Binning

Log and power transformations

Categorical data transformation

Encoding

Missing values for categorical data transformation

Text preprocessing

Feature selection

Excluding features with low variance

Univariate feature selection

Recursive feature elimination

Feature selection using random forest

Feature selection using dimensionality reduction

Principal Component Analysis

Feature generation

Chapter 4: Automated Algorithm Selection

Computational complexity

Big O notation

Differences in training and scoring time

Simple measure of training and scoring time

Code profiling in Python

Visualizing performance statistics

Implementing k-NN from scratch

Profiling your Python script line by line

Linearity versus non-linearity

Drawing decision boundaries

Decision boundary of logistic regression

The decision boundary of random forest

Commonly used machine learning algorithms

Necessary feature transformations

Supervised ML

Default configuration of auto-sklearn

Finding the best ML pipeline for product line prediction

Finding the best machine learning pipeline for network anomaly detection

Unsupervised AutoML

Commonly used clustering algorithms

Creating sample datasets with sklearn

K-means algorithm in action

The DBSCAN algorithm in action.

Agglomerative clustering algorithm in action

Simple automation of unsupervised learning

Visualizing high-dimensional datasets

Principal Component Analysis in action

t-SNE in action

Adding simple components together to improve the pipeline

Chapter 5: Hyperparameter Optimization

Hyperparameters

Warm start

Bayesian-based hyperparameter tuning

An example system

Chapter 6: Creating AutoML Pipelines

An introduction to machine learning pipelines

A simple pipeline

FunctionTransformer

A complex pipeline

Chapter 7: Dive into Deep Learning

Overview of neural networks

Neuron

Activation functions

The step function

The sigmoid function

The ReLU function

The tanh function

A feed-forward neural network using Keras

Autoencoders

Convolutional Neural Networks

Why CNN?

What is convolution?

What are filters?

The convolution layer

The ReLU layer

The pooling layer

The fully connected layer

Chapter 8: 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

Business understanding

Data understanding

Data preparation

Modeling

Evaluation

Deployment

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Index.

Notes:

Description based on print version record.

OCLC:

1034626960