Introduction to Machine Learning with Python: A Guide for Data Scientists (Paperback)

Andreas C. Müller, Sarah Guido

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商品描述

Description

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn:

  • Fundamental concepts and applications of machine learning
  • Advantages and shortcomings of widely used machine learning algorithms
  • How to represent data processed by machine learning, including which data aspects to focus on
  • Advanced methods for model evaluation and parameter tuning
  • The concept of pipelines for chaining models and encapsulating your workflow
  • Methods for working with text data, including text-specific processing techniques
  • Suggestions for improving your machine learning and data science skills

商品描述(中文翻譯)

描述

機器學習已成為許多商業應用和研究項目的重要組成部分,但這個領域並不僅限於擁有廣泛研究團隊的大公司。如果您使用Python,即使是初學者,本書也將教您建立自己的機器學習解決方案的實用方法。在當今數據爆炸的時代,機器學習應用只受您的想像力限制。

您將學習使用Python和scikit-learn庫創建成功的機器學習應用所需的步驟。作者Andreas Müller和Sarah Guido專注於使用機器學習算法的實際方面,而不是其背後的數學原理。熟悉NumPy和matplotlib庫將幫助您更好地理解本書內容。

通過本書,您將學到以下內容:

- 機器學習的基本概念和應用
- 廣泛使用的機器學習算法的優點和缺點
- 如何表示機器學習處理的數據,包括應該關注哪些數據方面
- 模型評估和參數調整的高級方法
- 用於鏈接模型和封裝工作流程的管道概念
- 處理文本數據的方法,包括特定於文本的處理技術
- 提高機器學習和數據科學技能的建議