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portada Applied Machine Learning: Using Machine Learning to Solve Business Problems
Type
Physical Book
Publisher
Author
Language
English
Pages
440
Format
Paperback
Dimensions
15.2 x 17.5 x 1.5 cm
ISBN13
9781493227587

Applied Machine Learning: Using Machine Learning to Solve Business Problems

Hodson, Jason (Author) · Rheinwerk Computing · Paperback

Applied Machine Learning: Using Machine Learning to Solve Business Problems - Hodson, Jason

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Synopsis "Applied Machine Learning: Using Machine Learning to Solve Business Problems"

Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following three 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 impact over time. With sample code for download, this book has everything you need to implement machine learning models for your business! In this book, you'll learn about: a. Data PreparationThe first step is to understand your data. Learn about the 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.b. Model Selection Choose the machine learning model that suits your needs! Follow a model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, clustering, and ensembling.c. Evaluation and IterationAssess and improve the quality of your model! Apply a variety of validation metrics to your model and enhance interpretability to avoid black box code. Then iterate through feature engineering and adding or removing data. d. Implementation and MonitoringYour model is ready to go--now see it in action! Learn how to implement the model to make predictions, monitor its performance, and measure its impact for your business. Highlights include: 1) Real-world use cases2) Data exploration3) Data cleaning4) Model decision framework5) Regression algorithms6) Decision trees7) Clustering8) Validation metrics9) Model iteration 10) Interpretability11) Implementation12) Monitoring

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