Machine Learning with R

This book helps readers understand the mathematics of  machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it’s applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning.

In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation.

The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.

這本書幫助讀者理解機器學習的數學原理，並在不同情境中應用。它分為兩個基本部分，第一部分介紹讀者線性代數、概率和數據分佈的理論，以及它們在機器學習中的應用。它還詳細介紹了機器學習的概念和限制，以及設計學習算法所涉及的內容。這部分幫助讀者理解機器學習的數學和統計方面。

轉而言之，第二部分討論了監督學習和非監督學習中使用的算法。它對每個學習算法進行數學推導，並使用R編碼來生成定制的學習應用程序。在此過程中，它觸及了每個算法的具體細節和背後的科學原理。

這本書包含大量的實例和R代碼。它解釋了每個算法的代碼，讀者可以修改代碼以滿足自己的需求。這本書對於打算使用R進行機器學習的所有研究人員以及對實施數據分析的學習算法的實際方面感興趣的人都很有價值。此外，對於那些難以將數學和統計概念與機器學習相關聯的人來說，它將特別有用和有啟發性。