Single volume reference on using various aspects of data science to evaluate, understand, and solve business problems A reference book for anyone in the field of data science, Applied Machine Learning for Data Science Practitioners walks readers through the end-to-end process of solving any machine learning problem by identifying, choosing, and applying the right solution for the issue at hand. The text enables readers to figure out optimal validation techniques based on the use case and data orientation, choose a range of pertinent models from different types of learning, and score models to apply metrics across all the estimators evaluated. Unlike most books on data science in today's market that jump right into algorithms and coding and focus on the most-used algorithms, this text helps data scientists evaluate all pertinent techniques and algorithms to assess all these machine learning problems and suitable solutions. Readers can make an informed decision on which models and validation techniques to use based on the business problem, data availability, desired outcome, and more. Written by an internationally recognized author in the field of data science, Applied Machine Learning for Data Science Practitioners also covers topics such as: Data preparation, including basic data cleaning, integration, transformation, and compression methods, along with data visualization and exploratory analyses Cross-validation in model validation techniques, including independent, identically distributed, imbalanced, blocked, and grouped data Prediction using regression models and classification using classification models, with applicable performance measurements for each Types of clustering in clustering models based on partition, hierarchy, fuzzy theory, distribution, density, and graph theory Detecting anomalies, including types of anomalies and key terms like noise, rare events, and outliers Applied Machine Learning for Data Science Practitioners is an essential resource for all data scientists and business professionals to cross-validate a range of different algorithms to find an optimal solution. Readers are assumed to have a basic understanding of solving business problems using data, high school level math, statistics, and coding skills.