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Machine Learning Algorithms

Machine Learning Algorithms

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Machine Learning Algorithms

Machine Learning Algorithms

Overview of this book

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.
Table of Contents (19 chapters)
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Introducing Recommendation Systems

Imagine an online shop with thousands of items. If you're not a registered user, you'll probably see a home page with some highlights, but if you've already bought some items, it would be interesting if the website showed products that you would probably buy, instead of a random selection. This is the purpose of a recommender system, and, in this chapter, we're going to discuss the most common techniques to create such a system.

The basic concepts are users, items, and ratings (or implicit feedback about the products, such as the fact you have bought them). Every model must work with known data (as in a supervised scenario) to be able to suggest the most suitable items or to predict ratings for all the items not evaluated yet.

We're going to discuss two different kinds of strategies:

  • User- or content-based
  • Collaborative...
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