What this book covers
Chapter 1, Introduction to Machine Learning, introduces machine learning concepts.
Chapter 2, Linear Regression, introduces and implements several linear regression models using F#.
Chapter 3, Classification Techniques, introduces classification as a formal problem and then solves some use cases using F#.
Chapter 4, Information Retrieval, provides implementations of several information retrieval distance metrics that can be useful in several situations.
Chapter 5, Collaborative Filtering, explains the workhorse algorithm for recommender systems, provides an implementation using F#, and then shows how to evaluate such a system.
Chapter 6, Sentiment Analysis, explains sentiment analysis and after positioning it as a formal problem statement, solves it using several state-of-the-art algorithms.
Chapter 7, Anomaly Detection, explains and poses the anomaly detection problem statement and then gives several algorithms and their implementation in F#.
