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Key benefits

Explore statistics and complex mathematics for data-intensive applications

Discover new developments in EM algorithm, PCA, and bayesian regression

Study patterns and make predictions across various datasets

Description

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.

What you will learn

Study feature selection and the feature engineering process

Assess performance and error trade-offs for linear regression

Build a data model and understand how it works by using different types of algorithm

Learn to tune the parameters of Support Vector Machines (SVM)

Explore the concept of natural language processing (NLP) and recommendation systems

Create a machine learning architecture from scratch

What do you get with Print?

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Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

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Frequently bought together

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Machine Learning Algorithms: Popular algorithms for data science and machine learning , Second Edition

What do you get with Print?