Applied Machine Learning: A Practical Guide to Preparing Data, Selecting Algorithms, and Implementing Machine Learning Models in the Real World (Rheinwerk Computing) New Edition

Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following compelling use cases, each with its own dataset. Get started with tools like GitHub and Anaconda, and then follow detailed instructions to prepare your data, select your model, evaluate its results, and measure its business impact over time. With sample code for download, this book gives you everything needed to implement machine learning models that solve real business problems!Practical introduction to applied machine learning across three real-world use casesSelect and implement the right machine learning model for your business problemEvaluate model results and monitor your models long termData PreparationThe first step is to understand your data. Learn about different data sources, and then explore your data through visualization, descriptive statistics, and correlation analysis. Clean up your data by identifying errors, writing dummy code, and more.Model SelectionChoose the machine learning model that fits your problem! Follow a structured model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, and clustering.Evaluation and IterationAssess and improve the quality of your model! Apply a variety of validation metrics, enhance interpretability to avoid black box code, and iterate through feature engineering and adding or removing data.Implementation and MonitoringYour model is ready—now put it to work! Learn how to implement your model to generate predictions, monitor its performance over time, and measure its impact for your business. Read more