Advanced Machine Learning: Fundamentals and algorithms (English Edition)(Paperback)
暫譯: 進階機器學習:基礎與演算法(英文版)(平裝本)
Kumar Tyagi, Amit, Tripathi, Khushboo, Kumar Sharma, Avinash
- 出版商: BPB Publications
- 出版日期: 2024-06-29
- 售價: $1,500
- 貴賓價: 9.5 折 $1,425
- 語言: 英文
- 頁數: 522
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9355516347
- ISBN-13: 9789355516343
-
相關分類:
Machine Learning
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相關主題
商品描述
DESCRIPTION
Our book is divided into several useful concepts and techniques of machine learning. This book serves as a valuable resource for individuals seeking to deepen their understanding of advanced topics in this field.
Learn about various learning algorithms, including supervised, unsupervised, and reinforcement learning, and their mathematical foundations. Discover the significance of feature engineering and selection for enhancing model performance. Understand model evaluation metrics like accuracy, precision, recall, and F1-score, along with techniques like cross-validation and grid search for model selection. Explore ensemble learning methods along with deep learning, unsupervised learning, time series analysis, and reinforcement learning techniques. Lastly, uncover real-world applications of the machine and deep learning algorithms.
After reading this book, readers will gain a comprehensive understanding of machine learning fundamentals and advanced techniques. With this knowledge, readers will be equipped to tackle real-world problems, make informed decisions, and develop innovative solutions using machine and deep learning algorithms.
WHAT YOU WILL LEARN
● Ability to tackle complex machine learning problems.
● Understanding of foundations, algorithms, ethical issues and how to implement each learning algorithm for their own use/ with their data.
● Efficient data analysis for real-time data will be understood by researchers/ students.
● Using data analysis in near future topics and cutting-edge technologies.
WHO THIS BOOK IS FOR
This book is ideal for students, professors, and researchers. It equips industry experts and academics with the technical know-how and practical implementations of machine learning algorithms.
商品描述(中文翻譯)
書籍描述
本書分為幾個有用的機器學習概念和技術。這本書是希望深入了解該領域進階主題的個人的寶貴資源。
了解各種學習算法,包括監督式學習、非監督式學習和強化學習,以及它們的數學基礎。發現特徵工程和特徵選擇對提升模型性能的重要性。理解模型評估指標,如準確率、精確率、召回率和F1-score,以及用於模型選擇的技術,如交叉驗證和網格搜索。探索集成學習方法,以及深度學習、非監督式學習、時間序列分析和強化學習技術。最後,揭示機器學習和深度學習算法的實際應用。
閱讀本書後,讀者將全面了解機器學習的基本原理和進階技術。憑藉這些知識,讀者將能夠解決現實世界中的問題,做出明智的決策,並利用機器學習和深度學習算法開發創新的解決方案。
您將學到的內容
● 解決複雜機器學習問題的能力。
● 理解基礎、算法、倫理問題以及如何為自己的使用/數據實施每個學習算法。
● 研究人員/學生將理解實時數據的高效數據分析。
● 在未來的主題和尖端技術中使用數據分析。
本書適合誰閱讀
本書非常適合學生、教授和研究人員。它為行業專家和學術界提供了機器學習算法的技術知識和實際實施。