40 Algorithms Every Data Scientist Should Know: Navigating through essential AI and ML algorithms (English Edition)
暫譯: 每位資料科學家必知的40種演算法:探索關鍵的AI與ML演算法 (英文版)

Weichenberger, Jürgen, Kwon, Huw

  • 出版商: BPB Publications
  • 出版日期: 2024-09-07
  • 售價: $1,710
  • 貴賓價: 9.5$1,625
  • 語言: 英文
  • 頁數: 588
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9355519834
  • ISBN-13: 9789355519832
  • 相關分類: 人工智慧Algorithms-data-structures
  • 海外代購書籍(需單獨結帳)

商品描述

DESCRIPTION

Mastering AI and ML algorithms is essential for data scientists. This book covers a wide range of techniques, from supervised and unsupervised learning to deep learning and reinforcement learning. This book is a compass to the most important algorithms that every data scientist should have at their disposal when building a new AI/ML application.

It offers a thorough introduction to AI and ML, covering key concepts, data structures, and various algorithms like linear regression, decision trees, and neural networks. It explores learning techniques like supervised, unsupervised, and semi-supervised learning and applies them to real-world scenarios such as natural language processing and computer vision. With clear explanations, code examples, and detailed descriptions of 40 algorithms, including their mathematical foundations and practical applications, this resource is ideal for both beginners and experienced professionals looking to deepen their understanding of AI and ML.

The final part of the book gives an outlook for more state-of-the-art algorithms that will have the potential to change the world of AI and ML fundamentals.


KEY FEATURES

● Covers a wide range of AI and ML algorithms, from foundational concepts to advanced techniques.

● Includes real-world examples and code snippets to illustrate the application of algorithms.

● Explains complex topics in a clear and accessible manner, making it suitable for learners of all levels.


WHAT YOU WILL LEARN

● Differences between supervised, unsupervised, and reinforcement learning.

● Gain expertise in data cleaning, feature engineering, and handling different data formats.

● Learn to implement and apply algorithms such as linear regression, decision trees, neural networks, and support vector machines.

● Learn to approach AI and ML challenges with a structured and analytical mindset.


WHO THIS BOOK IS FOR

This book is ideal for data scientists, ML engineers, and anyone interested in entering the world of AI.


商品描述(中文翻譯)

描述
掌握人工智慧 (AI) 和機器學習 (ML) 演算法對於資料科學家來說至關重要。本書涵蓋了從監督式學習到非監督式學習,再到深度學習和強化學習的廣泛技術。本書是每位資料科學家在建立新的 AI/ML 應用程式時應具備的最重要演算法的指南。

本書提供了對 AI 和 ML 的全面介紹,涵蓋了關鍵概念、資料結構以及各種演算法,如線性回歸、決策樹和神經網絡。它探討了監督式、非監督式和半監督式學習等學習技術,並將其應用於自然語言處理和計算機視覺等現實場景。透過清晰的解釋、程式碼範例和 40 種演算法的詳細描述,包括其數學基礎和實際應用,這本資源非常適合初學者和希望深化對 AI 和 ML 理解的經驗豐富的專業人士。

本書的最後一部分展望了更多尖端演算法,這些演算法有潛力改變 AI 和 ML 基礎的世界。



主要特點
● 涵蓋從基礎概念到進階技術的廣泛 AI 和 ML 演算法。
● 包含現實世界的範例和程式碼片段,以說明演算法的應用。
● 以清晰易懂的方式解釋複雜主題,適合各級學習者。


您將學到什麼
● 監督式、非監督式和強化學習之間的差異。
● 獲得資料清理、特徵工程和處理不同資料格式的專業知識。
● 學習實現和應用如線性回歸、決策樹、神經網絡和支持向量機等演算法。
● 學習以結構化和分析的思維方式來應對 AI 和 ML 挑戰。


本書適合誰
這本書非常適合資料科學家、機器學習工程師以及任何有興趣進入 AI 世界的人。