Preface

Machine learning (ML) is more prevalent now than ever before. Every day a lot of data is being generated. Machine learning algorithms perform heavy duty number crunching to improve our lives every day. The following image captures the major tasks that machine learning algorithms perform. These are the classes or types of problems that ML algorithms solve.

Our lives are more and more driven by the output of these ML algorithms than we care to admit. Let me walk you through the image once:

Computers everywhere: Now your smartphone can beat a vintage supercomputer, and computer are everywhere: in your phone, camera, car, microwave, and so on.
Clustering: Clustering is the task of identifying groups of items from a given list that seem to be similar to the others in the group. Clustering has many diverse uses. However, it is heavily used in market segment analysis to identify different categories of customers.
Classification: This is the ML algorithm that works hard to keep your spam e-mails away from your priority inbox. The same algorithm can be used to identify objects from images or videos and surprisingly, the same algorithm can be used to predict whether a patient has cancer or not. Generally, a lot of data is provided to the algorithm, from which it learns. That's why this set of algorithms is sometime referred to as supervised learning algorithms, and this constitutes the vast majority of machine learning algorithms.
Predictions: There are several ML algorithms that perform predictions for several situations that are important in life. For example, there are predictors that predict fuel price in the near future. This family of algorithms is known as regressions.
Anomaly detection: Anomaly, as the name suggests, relates to items that have attributes that are not similar to normal ones. Anomaly detection algorithms use statistical methods to find out the anomalous items from a given list automatically. This is an example of unsupervised learning. Anomaly detection has several diverse uses, such as finding faulty items in factories to finding intruders on a video stream coming from a surveillance camera, and so on.
Recommendations: Every time you visit Amazon and rate a product, the site recommends some items to you. Under the hood is a clever machine learning algorithm in action called collaborative filtering, which takes cues from other users purchasing similar items as you are. Recommender systems are a very active research topic now and several other algorithms are being considered.
Sentiment analysis: Whenever a product hits the market, the company that brought it into the market wants to know how the market is reacting towards it. Is it positive or negative? Sentiment analysis techniques help to identify these reactions. Also, in review websites, people post several comments, and the website might be interested in publishing a generalized positive or negative rating for the item under review. Here, sentiment analysis techniques can be quite helpful.
Information retrieval: Whenever you hit the search button on your favorite search engine, a plethora of information retrieval algorithms are used under the hood. These algorithms are also used in the content-based filtering that is used in recommender systems.

Now that you have a top-level idea of what ML algorithms can do for you, let's see why F# is the perfect fit for the implementations. Here are my reasons for using F# to implement machine learning algorithms:

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F# for Machine Learning Essentials

By : Sudipta Mukherjee

F# for Machine Learning Essentials

By: Sudipta Mukherjee

Overview of this book

Preface

F# for Machine Learning Essentials

By : Sudipta Mukherjee

F# for Machine Learning Essentials

By: Sudipta Mukherjee

Overview of this book

Preface

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