Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

Giuseppe Bonaccorso

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商品描述

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

  • Explore statistics and complex mathematics for data-intensive applications
  • Discover new developments in EM algorithm, PCA, and bayesian regression
  • Study patterns and make predictions across various datasets

Book Description

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight.

This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you'll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture.

By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.

What you will learn

  • Study feature selection and the feature engineering process
  • Assess performance and error trade-offs for linear regression
  • Build a data model and understand how it works by using different types of algorithm
  • Learn to tune the parameters of Support Vector Machines (SVM)
  • Explore the concept of natural language processing (NLP) and recommendation systems
  • Create a machine learning architecture from scratch

Who this book is for

Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book.

Table of Contents

  1. A Gentle Introduction to Machine Learning
  2. Important Elements in Machine Learning
  3. Feature Selection and Feature Engineering
  4. Regression Algorithms
  5. Linear Classification Algorithms
  6. Naive Bayes and Discriminant Analysis
  7. Support Vector Machines
  8. Decision Trees and Ensemble Learning
  9. Clustering Fundamentals
  10. Advanced Clustering
  11. Hierarchical Clustering
  12. Introducing Recommendation Systems
  13. Introducing Natural Language Processing
  14. Topic Modeling and Sentiment Analysis in NLP
  15. Introducing Neural Networks
  16. Advanced Deep Learning Models
  17. Creating a Machine Learning Architecture

商品描述(中文翻譯)

一本易於理解、逐步指南,幫助讀者掌握機器學習演算法在實際應用中的技巧。

主要特點:
- 探索統計學和複雜數學在數據密集型應用中的應用
- 發現 EM 演算法、主成分分析 (PCA) 和貝葉斯回歸等新發展
- 研究模式並對各種數據集進行預測

書籍描述:
機器學習因其在大數據集上進行強大且快速的預測而廣受歡迎。然而,其強大輸出背後的真正力量是複雜的演算法,涉及大量的統計分析,能夠處理大型數據集並生成深入的洞察。

《機器學習演算法》第二版將引導讀者了解與機器學習演算法相關的重要發展成果,這些成果對機器學習過程做出了重大貢獻,並幫助讀者在監督、半監督和強化學習領域加強和掌握統計解釋能力。在介紹演算法的核心概念後,讀者將通過使用最常用的庫(如scikit-learn、NLTK、TensorFlow和Keras)探索基於真實世界的示例。讀者將了解主成分分析(PCA)、獨立成分分析(ICA)、貝葉斯回歸、判別分析、高級聚類和高斯混合等新主題。

通過閱讀本書,您將學習機器學習演算法,並能夠將其應用於實際生產中,使您的機器學習應用更具創新性。

學到的內容:
- 學習特徵選擇和特徵工程過程
- 評估線性回歸的性能和錯誤折衷
- 構建數據模型,並通過使用不同類型的演算法來理解其工作原理
- 學習調整支持向量機(SVM)的參數
- 探索自然語言處理(NLP)和推薦系統的概念
- 從頭開始創建機器學習架構

適合對象:
《機器學習演算法》適合機器學習工程師、數據工程師或初級數據科學家,他們希望在預測分析和機器學習領域取得進一步發展。熟悉 R 和 Python 將有助於更好地理解本書內容。

目錄:
1. 機器學習簡介
2. 機器學習的重要元素
3. 特徵選擇和特徵工程
4. 回歸演算法
5. 線性分類演算法
6. 朴素貝葉斯和判別分析
7. 支持向量機
8. 決策樹和集成學習
9. 聚類基礎知識
10. 高級聚類
11. 階層聚類
12. 推薦系統介紹
13. 自然語言處理介紹
14. NLP 中的主題建模和情感分析
15. 神經網絡介紹
16. 高級深度學習模型
17. 創建機器學習架構