Introduction To Machine Learning With R: Rigorous Mathematical Analysis

Introduction To Machine Learning With R: Rigorous Mathematical Analysis

Price : 53.07

Ends on : N/A

View on eBay
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 explore the basics of machine learning with R, a popular programming language for statistical analysis and data visualization.

To truly understand machine learning, it’s important to have a solid foundation in mathematics. In this introduction, we will delve into the rigorous mathematical analysis behind machine learning algorithms and how they work in R.

Key topics that will be covered include:

– Linear algebra: Understanding matrices and vectors, as well as operations such as matrix multiplication and transposition.
– Calculus: Grasping concepts such as differentiation and integration, which are crucial for optimizing machine learning models.
– Probability theory: Learning about probability distributions, random variables, and how they are used in machine learning.

We will also delve into specific machine learning algorithms, such as linear regression, logistic regression, and decision trees, and demonstrate how they can be implemented in R.

By the end of this post, you will have a solid understanding of the mathematical principles behind machine learning and how to apply them in R. Stay tuned for more in-depth tutorials and practical examples in future posts!
#Introduction #Machine #Learning #Rigorous #Mathematical #Analysis,machine learning: an applied mathematics introduction