The fundamental aim of this book is help its readers quickly deploy, optimize, and evaluate every kind of machine learning algorithm that scikit-learn provides in an agile manner.
Readers will learn how to deploy supervised machine learning algorithms, such as logistic regression, k-nearest neighbors, linear regression, Support Vector Machines, Naive Bayes, and tree-based algorithms, in order to solve classification and regression machine learning problems.
Readers will also learn how to deploy unsupervised machine learning algorithms such as the k-means algorithm in order to cluster unlabeled data into groups.
Finally, readers will be provided with different techniques to visually interpret and evaluate the performance of the algorithms that they build.
