Machine Learning with R

Help readers understand the mathematical interpretation of learning algorithms Teach the basics of linear algebra, probability, and data distributions and how they are essential in formulating a learning algorithm
Help readers construct and modify their own learning algorithms, such as ridge and lasso regression, decision trees, boosted trees, k-nearest neighbors, etc

Help readers understand the mathematical interpretation of learning algorithms Teach the basics of linear algebra, probability, and data distributions and how they are essential in formulating a learning algorithm Help readers construct and modify their own learning algorithms, such as ridge and lasso regression, decision trees, boosted trees, k-nearest neighbors, etc Includes supplementary material: sn.pub/extras

Autorentext
Abhijit Ghatak is a Data Scientist and holds an ME in Engineering and MS in Data Science from Stevens Institute of Technology, USA. He started his career as a submarine engineer officer in the Indian Navy and worked on multiple data-intensive projects involving submarine operations and construction. He has worked in academia, technology companies and as a research scientist in the area of Internet of Things (IoT) and pattern recognition for the European Union (EU). He has published in the areas of engineering and machine learning and is presently a consultant in the area of pattern recognition and data analytics. His areas of research include IoT, stream analytics and design of deep learning systems.

Inhalt
Chapter 1. Linear Algebra, Numerical Optimization and it's Applications in Machine Learning.- Chapter 2. Probability and Distributions.- Chapter 3. Introduction to Machine Learning.- Chapter 4. Regression.- Chapter 5. Classification.- Chapter 6. Clustering.

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