Chapter 4. Machine Learning

After having illustrated all the data preparation steps in a data science project, we have finally arrived at the learning phase where algorithms are applied. In order to introduce you to the most effective machine learning tools that are readily available in Scikit-learn, we have prepared a brief introduction for all the major families of algorithms, complete with examples and tips on the hyper-parameters that guarantee the best possible results.

In this chapter, we will present the following topics:

• Linear and logistic regression
• Naive Bayes
• The k-Nearest Neighbors (kNN)
• Support Vector Machines (SVM)
• Ensembles such as Random Forests and Gradient Tree Boosting
• Stochastic gradient-based classification and regression for big data
• Unsupervised clustering with K-means and DBSCAN