Suppose you want to predict whether tomorrow will be a sunny or rainy day. You can develop an algorithm that is based on the current weather and your meteorological knowledge using a rather complicated set of rules to return the desired prediction. Now suppose that you have a record of the day-by-day weather conditions for the last five years, and you find that every time you had two sunny days in a row, the following day also happened to be a sunny one. Your algorithm could generalize this and predict that tomorrow will be a sunny day since the sun reigned today and yesterday. This algorithm is a pretty simple example of learning from experience. This is what Machine Learning is all about: algorithms that learn from the available data.
In this book, you will learn several methods for building Machine Learning applications that solve different real-world tasks, from document classification to image recognition.
We will use Python, a simple, popular, and widely used programming language, and scikit-learn, an open source Machine Learning library. In each chapter, we will present a different Machine Learning setting and a couple of well-studied methods as well as show step-by-step examples that use Python and scikit-learn to solve concrete tasks. We will also show you tips and tricks to improve algorithm performance, both from the accuracy and computational cost point of views.
What this book covers
Chapter 1, Machine Learning – A Gentle Introduction, presents the main concepts behind Machine Learning while solving a simple classification problem: discriminating flower species based on its characteristics.
Chapter 2, Supervised Learning, introduces four classification methods: Support Vector Machines, Naive Bayes, decision trees, and Random Forests. These methods are used to recognize faces, classify texts, and explain the causes for surviving from the Titanic accident. It also presents Linear Models and revisits Support Vector Machines and Random Forests, using them to predict house prices in Boston.
Chapter 3, Unsupervised Learning, describes methods for dimensionality reduction with Principal Component Analysis to visualize high dimensional data in just two dimensions. It also introduces clustering techniques to group instances of handwritten digits according to a similarity measure using the k-means algorithm.
Chapter 4, Advanced Features, shows how to preprocess the data and select the best features for learning, a task called Feature Selection. It also introduces Model Selection: selecting the best method parameters using the available data and parallel computation.
