scikit-learn Cookbook - Second Edition: Over 80 recipes for machine learning in Python with scikit-learn

Julian Avila

  • 出版商: Packt Publishing
  • 出版日期: 2017-11-15
  • 售價: $1,760
  • 貴賓價: 9.5$1,672
  • 語言: 英文
  • 頁數: 374
  • 裝訂: Paperback
  • ISBN: 178728638X
  • ISBN-13: 9781787286382
  • 相關分類: Python程式語言Machine Learning
  • 海外代購書籍(需單獨結帳)
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商品描述

Learn to use scikit-learn operations and functions for Machine Learning and deep learning applications.

About This Book

  • Handle a variety of machine learning tasks effortlessly by leveraging the power of scikit-learn
  • Perform supervised and unsupervised learning with ease, and evaluate the performance of your model
  • Practical, easy to understand recipes aimed at helping you choose the right machine learning algorithm

Who This Book Is For

Data Analysts already familiar with Python but not so much with scikit-learn, who want quick solutions to the common machine learning problems will find this book to be very useful. If you are a Python programmer who wants to take a dive into the world of machine learning in a practical manner, this book will help you too.

What You Will Learn

  • Build predictive models in minutes by using scikit-learn
  • Understand the differences and relationships between Classification and Regression, two types of Supervised Learning.
  • Use distance metrics to predict in Clustering, a type of Unsupervised Learning
  • Find points with similar characteristics with Nearest Neighbors.
  • Use automation and cross-validation to find a best model and focus on it for a data product
  • Choose among the best algorithm of many or use them together in an ensemble.
  • Create your own estimator with the simple syntax of sklearn
  • Explore the feed-forward neural networks available in scikit-learn

In Detail

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively.

The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model.

By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.

Style and Approach

This book consists of practical recipes on scikit-learn that target novices as well as intermediate users. It goes deep into the technical issues, covers additional protocols, and many more real-live examples so that you are able to implement it in your daily life scenarios.

商品描述(中文翻譯)

學習使用scikit-learn操作和函數進行機器學習和深度學習應用。

關於本書
- 利用scikit-learn的強大功能輕鬆處理各種機器學習任務
- 輕鬆執行監督學習和非監督學習,並評估模型的性能
- 提供實用、易於理解的食譜,幫助您選擇合適的機器學習算法

本書適合對Python有一定了解但對scikit-learn不太熟悉的數據分析師,他們希望快速解決常見的機器學習問題。如果您是一名Python程序員,想以實用的方式深入研究機器學習,本書也將對您有所幫助。

您將學到什麼
- 使用scikit-learn在幾分鐘內建立預測模型
- 了解分類和回歸兩種監督學習的差異和關係
- 使用距離度量在聚類中進行預測,這是一種非監督學習方法
- 找到具有相似特徵的點,使用最近鄰算法
- 使用自動化和交叉驗證找到最佳模型,並專注於數據產品
- 從多個最佳算法中選擇,或將它們結合在一起形成集成算法
- 使用sklearn的簡單語法創建自己的估計器
- 探索scikit-learn中提供的前向神經網絡

詳細內容
由於其簡單性和靈活性,Python迅速成為分析師和數據科學家的首選語言,在Python數據領域中,scikit-learn是機器學習的不二之選。本書提供了解決機器學習中常見和不太常見問題的步驟和解決方案,以及如何有效地利用scikit-learn執行各種機器學習任務。

第二版首先介紹了評估數據的統計特性並生成用於機器學習建模的合成數據的食譜。隨著章節的進展,您將學習實施數據預處理、線性回歸、邏輯回歸、K-NN、朴素貝葉斯、分類、決策樹、集成等技術的食譜。此外,您還將學習使用多類分類、交叉驗證、模型評估等優化模型,並深入研究如何使用scikit-learn實現深度學習。除了涵蓋模型部分的增強功能、API和新功能(如分類器、回歸器和估計器)外,本書還包含評估和微調模型性能的食譜。

通過閱讀本書,您將探索scikit-learn為Python提供的豐富功能,以解決您遇到的任何機器學習問題。

風格和方法
本書包含針對scikit-learn的實用食譜,旨在針對初學者和中級用戶。它深入探討技術問題,涵蓋其他協議和更多實際示例,以便您能夠在日常生活場景中實施它。