Hands-On Automated Machine Learning: A beginner's guide to building automated machine learning systems using AutoML and Python

Sibanjan Das, Umit Mert Cakmak

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商品描述

Automate data and model pipelines for faster machine learning applications

Key Features

  • 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

Book Description

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.

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

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.

Table of Contents

  1. Introduction to AutoML
  2. Introduction to Machine Learning Using Python
  3. Data Preprocessing
  4. Automated Algorithm Selection
  5. Hyperparameter Optimization
  6. Creating AutoML pipelines
  7. Dive into Deep Learning
  8. Critical Aspects of ML and Data Science Projects

商品描述(中文翻譯)

自動化數據和模型流程以加快機器學習應用程式的速度

主要特點:
- 構建不同機器學習組件的自動化模塊
- 深入了解機器學習流程的每個組件
- 學習使用不同的開源自動機器學習(AutoML)和特徵工程平台

書籍描述:
自動機器學習(AutoML)旨在自動化機器學習的部分過程。現有的AutoML工具使得數據科學從業者的工作變得輕鬆,並在高級分析社區中受到好評。《自動化機器學習》涵蓋了創建自動化機器學習模塊所需的基礎知識,並以最實用的方式幫助您快速上手。

在本書中,您將學習如何自動化機器學習流程中的不同任務,例如數據預處理、特徵選擇、模型訓練、模型優化等。此外,它還演示了如何使用可用的自動化庫(如auto-sklearn和MLBox),並創建和擴展自己的自定義AutoML組件。

通過閱讀本書,您將更清楚地了解自動化機器學習的不同方面,並能夠使用實際數據集來進行自動化任務。您可以利用本書的學習成果在項目中實施機器學習,並更接近贏得各種機器學習競賽的目標。

您將學到什麼:
- 理解自動化機器學習系統的基本原理
- 探索auto-sklearn和MLBox進行自動機器學習任務
- 自動化預處理方法以及特徵轉換
- 使用Python堆棧增強特徵選擇和生成
- 將ML的各個組件組合成完整的AutoML框架
- 解密超參數調整以優化ML模型
- 深入研究神經網絡和自編碼器等機器學習概念
- 理解與AutoML相關的信息成本和權衡

本書適合對自動機器學習概念尚不熟悉的初級數據科學家、數據分析師或機器學習愛好者。如果您是ML工程師或數據專業人士,並且有興趣為項目開發快速的機器學習流程,本書也將對您有所幫助。具備Python編程的先備知識將有助於您充分利用本書的內容。

目錄:
1. AutoML簡介
2. 使用Python介紹機器學習
3. 數據預處理
4. 自動算法選擇
5. 超參數優化
6. 創建AutoML流程
7. 深入深度學習
8. ML和數據科學項目的關鍵方面