Now in its second edition of this book shows how the algorithms and techniques offered by machine learning can be used to automate any analytical model. Author Gavin Hackeling examines a variety of machine learning models including popular machine learning algorithms including k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods.

<ASIN:1788299876>

The book shows how to build systems that classify documents, recognize images, detect ads, and more. It also covers scikit-learn’s API for extracting features from categorical variables, text and images and evaluating model performance.

Author: Gavin Hackeling
Publisher: Packt Publishing
Date: July 2017
Pages: 254
ISBN: 978-1788299879
Print: 1788299876
Kindle: B06ZYRPFMZ
Audience: developers interested in machine learning
Level: Intermediate
Category: Artificial Intelligence

Topics covered include

• Review fundamental concepts such as bias and variance
• Extract features from categorical variables, text, and images
• Predict the values of continuous variables using linear regression and K Nearest Neighbors
• Classify documents and images using logistic regression and support vector machines
• Create ensembles of estimators using bagging and boosting techniques
• Discover hidden structures in data using K-Means clustering
• Evaluate the performance of machine learning systems in common tasks

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