Learning scikit-learn: Machine Learning in Python (Paperback)

Raúl Garreta, Guillermo Moncecchi

  • 出版商: Packt Publishing
  • 出版日期: 2013-11-25
  • 售價: $1,310
  • 貴賓價: 9.5$1,245
  • 語言: 英文
  • 頁數: 118
  • 裝訂: Paperback
  • ISBN: 1783281936
  • ISBN-13: 9781783281930
  • 相關分類: Python程式語言Machine Learning
  • 海外代購書籍(需單獨結帳)

買這商品的人也買了...

商品描述

Incorporating machine learning in your applications is becoming essential. As a programmer this book is the ideal introduction to scikit-learn for your Python environment, taking your skills to a whole new level.

Overview

  • Use Python and scikit-learn to create intelligent applications
  • Apply regression techniques to predict future behaviour and learn to cluster items in groups by their similarities
  • Make use of classification techniques to perform image recognition and document classification

In Detail

Machine learning, the art of creating applications that learn from experience and data, has been around for many years. However, in the era of “big data”, huge amounts of information is being generated. This makes machine learning an unavoidable source of new data-based approximations for problem solving.

With Learning scikit-learn: Machine Learning in Python, you will learn to incorporate machine learning in your applications. The book combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. Ranging from handwritten digit recognition to document classification, examples are solved step by step using Scikit-learn and Python.

The book starts with a brief introduction to the core concepts of machine learning with a simple example. Then, using real-world applications and advanced features, it takes a deep dive into the various machine learning techniques.

You will learn to evaluate your results and apply advanced techniques for preprocessing data. You will also be able to select the best set of features and the best methods for each problem.

With Learning scikit-learn: Machine Learning in Python you will learn how to use the Python programming language and the scikit-learn library to build applications that learn from experience, applying the main concepts and techniques of machine learning.

What you will learn from this book

  • Set up scikit-learn inside your Python environment
  • Classify objects (from documents to human faces and flower species) based on some of their features, using a variety of methods from Support Vector Machines to Naïve Bayes
  • Use Decision Trees to explain the main causes of certain phenomenon such as the Titanic passengers’ survival
  • Predict house prices using regression techniques
  • Display and analyse groups in your data using dimensionality reduction
  • Make use of different tools to preprocess, extract, and select the learning features
  • Select the best parameters for your models using model selection
  • Improve the way you build your models using parallelization techniques

Approach

The book adopts a tutorial-based approach to introduce the user to Scikit-learn.

Who this book is written for

If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.

商品描述(中文翻譯)

將上述文字翻譯成繁體中文如下:

「在你的應用程式中加入機器學習已經變得不可或缺。作為一位程式設計師,這本書是你在 Python 環境中學習 scikit-learn 的理想入門,將你的技能提升到全新的水平。

概述:
- 使用 Python 和 scikit-learn 創建智能應用程式
- 應用迴歸技術來預測未來行為,並學習按相似性將項目分組
- 利用分類技術進行圖像識別和文件分類

詳細內容:
機器學習,即從經驗和數據中創建應用程式的藝術,已經存在多年。然而,在「大數據」時代,產生了大量的信息。這使得機器學習成為解決問題的新數據近似不可避免的來源。

通過《學習 scikit-learn:Python 中的機器學習》,您將學習將機器學習應用於您的應用程式中。本書結合了機器學習中一些主要概念和方法的介紹,並通過實際的實例解決了現實世界的問題。從手寫數字識別到文件分類,這些例子使用 Scikit-learn 和 Python 逐步解決。

本書以一個簡單的例子簡要介紹機器學習的核心概念。然後,使用真實應用和高級功能,深入探討了各種機器學習技術。

您將學習評估結果並應用高級技術來預處理數據。您還將能夠選擇每個問題的最佳特徵集和最佳方法。

通過《學習 scikit-learn:Python 中的機器學習》,您將學習如何使用 Python 編程語言和 scikit-learn 库來構建從經驗中學習的應用程式,應用機器學習的主要概念和技術。

本書的學習重點:
- 在 Python 環境中設置 scikit-learn
- 根據一些特徵對對象進行分類(從文件到人臉和花卉物種),使用各種方法,從支持向量機到朴素貝葉斯
- 使用決策樹解釋某些現象的主要原因,例如鐵達尼號乘客的生存情況
- 使用迴歸技術預測房價
- 使用降維技術顯示和分析數據中的群組
- 利用不同工具預處理、提取和選擇學習特徵
- 使用模型選擇選擇最佳參數
- 使用並行化技術改進模型構建方式

本書採用教學式的方法介紹 Scikit-learn。

本書適合對機器學習和基於數據的方法感興趣的程式設計師,以構建智能應用程式並提升編程技能。不需要具備機器學習算法的先前經驗。」