Introduction to Machine Learning with R: Rigorous Mathematical Analysis

Price: $47.49
(as of Dec 15,2024 23:59:47 UTC – Details)

From the brand

Explore further ‘R’ resources

Sharing the knowledge of experts

O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

ASIN ‏ : ‎ B07B9K66CL
Publisher ‏ : ‎ O’Reilly Media; 1st edition (March 7, 2018)
Publication date ‏ : ‎ March 7, 2018
Language ‏ : ‎ English
File size ‏ : ‎ 8004 KB
Simultaneous device usage ‏ : ‎ Unlimited
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 228 pages
Page numbers source ISBN ‏ : ‎ 1491976446

Machine learning is a powerful tool that allows computers to learn from data and make predictions or decisions without being explicitly programmed. In this post, we will provide an introduction to machine learning with R, a popular programming language for statistical computing and data analysis.

One of the key aspects of machine learning is the use of algorithms to analyze and learn from data. These algorithms can be broadly categorized into supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, where the correct output is known. In unsupervised learning, the algorithm is trained on unlabeled data and must find patterns or relationships within the data. Reinforcement learning involves training an algorithm to make sequential decisions in an environment to maximize a reward.

R is a powerful tool for implementing machine learning algorithms due to its extensive library of packages, such as caret, mlr, and randomForest, that provide implementations of various algorithms. In addition, R has a strong community of users and developers who contribute to the development of new algorithms and techniques.

One of the key components of machine learning is the rigorous mathematical analysis that underlies the algorithms. Understanding the mathematical principles behind machine learning algorithms is essential for developing a deep understanding of how they work and how to effectively apply them to real-world problems.

In this post, we will explore some of the fundamental mathematical concepts that underlie machine learning algorithms, such as linear algebra, calculus, probability theory, and optimization. We will also provide examples of how these concepts are used in popular machine learning algorithms, such as linear regression, logistic regression, support vector machines, and neural networks.

By understanding the rigorous mathematical analysis behind machine learning algorithms, you will be better equipped to implement and optimize these algorithms in R and apply them to real-world problems. Stay tuned for more posts on machine learning with R!
#Introduction #Machine #Learning #Rigorous #Mathematical #Analysis