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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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Important Elements in Machine Learning

In this chapter, we're going to discuss some important elements and approaches that span through all machine learning topics and also create a philosophical foundation for many common techniques. First of all, it's useful to understand the mathematical foundation of data formats and prediction functions. In most algorithms, these concepts are treated in different ways, but the goal is always the same. More recent techniques, such as deep learning, extensively use energy/loss functions, just like the one described in this chapter, and even if there are slight differences, a good machine learning result is normally associated with the choice of the best loss function and the use of the right algorithm to minimize it.

In particular, we will be discussing the following topics:

  • The generic structure of a machine learning problem and...
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