Machine Learning with Python for Everyone

Fenner, Mark

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商品描述

The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python

Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.

Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you'll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field's most sophisticated and exciting techniques. Whether you're a student, analyst, scientist, or hobbyist, this guide's insights will be applicable to every learning system you ever build or use.

  • Understand machine learning algorithms, models, and core machine learning concepts
  • Classify examples with classifiers, and quantify examples with regressors
  • Realistically assess performance of machine learning systems
  • Use feature engineering to smooth rough data into useful forms
  • Chain multiple components into one system and tune its performance
  • Apply machine learning techniques to images and text
  • Connect the core concepts to neural networks and graphical models
  • Leverage the Python scikit-learn library and other powerful tools

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商品描述(中文翻譯)

《Python機器學習系統完全入門指南》

《Python機器學習入門指南》將幫助您掌握構建有效學習系統所需的過程、模式和策略,即使您是完全初學者也沒問題。如果您能寫一些Python代碼,無論您對大學水平的數學了解多少,本書都適合您。主要講師Mark E. Fenner使用通俗易懂的故事、圖片和Python示例來傳達機器學習的概念。

Mark首先討論了機器學習及其能力,以平易近人的方式介紹了關鍵的數學和計算主題,並引導您進行構建、訓練和評估學習系統的第一步。逐步地,您將填寫實用學習系統的各個組件,擴展您的工具箱,並探索一些領域中最複雜且令人興奮的技術。無論您是學生、分析師、科學家還是愛好者,本指南的見解都適用於您構建或使用的每個學習系統。


  • 了解機器學習算法、模型和核心概念

  • 使用分類器對示例進行分類,使用回歸器對示例進行量化

  • 實際評估機器學習系統的性能

  • 使用特徵工程將粗糙數據轉換為有用形式

  • 將多個組件連接成一個系統並調整其性能

  • 將機器學習技術應用於圖像和文本

  • 將核心概念與神經網絡和圖形模型相結合

  • 利用Python的scikit-learn庫和其他強大工具

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作者簡介

Dr. Mark Fenner, owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function.

作者簡介(中文翻譯)

馬克·芬納博士是Fenner Training and Consulting有限責任公司的所有者,自1999年以來一直向不同的成年人群教授計算和數學,並擁有計算機科學博士學位。他的研究包括機器學習和數值算法的設計、實施和性能;開發檢測用戶異常的學習系統;以及蛋白質功能的概率建模。