Machine Learning with scikit-learn Quick Start Guide: Classification, regression, and clustering techniques in Python

Kevin Jolly

  • 出版商: Packt Publishing
  • 出版日期: 2018-10-31
  • 售價: $1,400
  • 貴賓價: 9.5$1,330
  • 語言: 英文
  • 頁數: 172
  • 裝訂: Paperback
  • ISBN: 1789343704
  • ISBN-13: 9781789343700
  • 相關分類: Python程式語言Machine Learning
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商品描述

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.

Key Features

  • Build your first machine learning model using scikit-learn
  • Train supervised and unsupervised models using popular techniques such as classification, regression and clustering
  • Understand how scikit-learn can be applied to different types of machine learning problems

Book Description

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.

This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.

Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.

What you will learn

  • Learn how to work with all scikit-learn's machine learning algorithms
  • Install and set up scikit-learn to build your first machine learning model
  • Employ Unsupervised Machine Learning Algorithms to cluster unlabelled data into groups
  • Perform classification and regression machine learning
  • Use an effective pipeline to build a machine learning project from scratch

Who this book is for

This book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.

Table of Contents

  1. Introducing Machine Learning with scikit-learn
  2. Predicting categories with K-Nearest Neighbours
  3. Predicting categories with Logistic Regression
  4. Predicting categories with Naive Bayes and SVMs
  5. Predicting numeric outcomes with Linear Regression
  6. Classification & Regression with Trees
  7. Clustering data with Unsupervised Machine Learning
  8. Performance evaluation methods

商品描述(中文翻譯)

使用scikit-learn部署監督和非監督機器學習算法,進行分類、回歸和聚類。

主要特點:
- 使用scikit-learn建立第一個機器學習模型
- 使用常用技術(如分類、回歸和聚類)訓練監督和非監督模型
- 了解scikit-learn如何應用於不同類型的機器學習問題

書籍描述:
Scikit-learn是Python編程語言的強大機器學習庫,提供了一組監督和非監督學習算法。本書是學習如何部署、優化和評估scikit-learn提供的所有重要機器學習算法的最簡單方法。

本書教你如何使用scikit-learn進行機器學習。你將從設置和配置scikit-learn的機器學習環境開始。為了使用scikit-learn,你將學習如何實現各種監督和非監督機器學習模型。你將學習分類、回歸和聚類技術,以處理不同類型的數據集並訓練模型。

最後,你將學習一個有效的流程,幫助你從頭開始構建機器學習項目。通過本書的學習,你將能夠自信地構建自己的機器學習模型,進行準確的預測。

你將學到什麼:
- 學習如何使用scikit-learn的所有機器學習算法
- 安裝和設置scikit-learn,建立第一個機器學習模型
- 使用非監督機器學習算法將無標籤數據聚類成群組
- 執行分類和回歸機器學習
- 使用有效的流程從頭開始構建機器學習項目

本書適合對scikit-learn感興趣的機器學習開發人員。具備Python編程的中級知識以及一些線性代數和概率的基礎知識將有所幫助。

目錄:
1. 使用scikit-learn介紹機器學習
2. 使用K-最近鄰算法預測類別
3. 使用邏輯回歸預測類別
4. 使用朴素貝葉斯和支持向量機預測類別
5. 使用線性回歸預測數值結果
6. 使用樹進行分類和回歸
7. 使用非監督機器學習對數據進行聚類
8. 性能評估方法