Statistic methods for machine learning
After reading this book, you will be able to: Use descriptive statistics and data visualizations to quickly and more deeply understand the shape and relationships in data. Use inferential statistical tests to quickly and effectively quantify the relationships between samples, such as the results of experiments with different predictive algorithms or differing configurations. Use estimation statistics to quickly and effectively quantify the confidence in estimated model skill and model predictions.
Statistics is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in statistics that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, and much more.