The study and application of machine learning and artificial intelligence has recently been the source of much interest and research in the technology and business communities. Advanced data analytics and machine learning techniques have shown great promise in advancing many sectors, such as personalized healthcare and self-driving cars, as well as in solving some of the world's greatest challenges, such as combating climate change. This book has been designed to assist you in taking advantage of the unique confluence of events in the field of data science and machine learning today. Across the globe, private enterprises and governments are realizing the value and efficiency of data-driven products and services. At the same time, reduced hardware costs and open source software solutions are significantly reducing the barriers to entry of learning and applying machine learning techniques.

Throughout this book, you will develop the skills required to identify, prepare, and build predictive models using supervised machine learning techniques in the Python programming language. The six chapters each cover one aspect of supervised learning. This chapter introduces a subset of the Python machine learning toolkit, as well as some of the things that need to be considered when loading and using data sources. This data exploration process is further explored in Chapter 2, Exploratory Data Analysis and Visualization, as we introduce exploratory data analysis and visualization. Chapter 3, Regression Analysis, and Chapter 4, Classification, look at two subsets of machine learning problems – regression and classification analysis – and demonstrate these techniques through examples. Finally, Chapter 5, Ensemble Modeling, covers ensemble networks, which use multiple predictions from different models to boost overall performance, while Chapter 6, Model Evaluation, covers the extremely important concepts of validation and evaluation metrics. These metrics provide a means of estimating the true performance of a model.