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Book Overview & Buying
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Table Of Contents
Hands-On Artificial Intelligence for IoT - Second Edition
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The goal of this chapter was to provide you with an intuitive understanding of different standard ML algorithms so that you can make an informed choice. We covered the popular ML algorithms used for classification and regression. We also learned how supervised and unsupervised learning are different from each other. Linear regression, logistic regression, SVM, naive Bayes, and decision trees were introduced, along with the fundamental principles involved in each. We used regression methods to predict the electrical power production of a thermal station and classification methods to classify wine as good or bad. Lastly, we covered the common problems with different ML algorithms and explored some tips and tricks to solve them.
In the upcoming chapter, we will delve into deep learning algorithms and witness their capabilities first hand.
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