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Low-Code AI: A Practical Project-Driven Introduction to Machine Learning
Gwendolyn Stripling, Michael AbelBusiness and data analysts get a project-based introduction to ML/AI using a detailed, data-driven approach: loading and analyzing data; feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications.
You'll learn how to
Distinguish between structured and unstructured data and the challenges they present
Visualize and analyze data
Preprocess data for input into a machine learning model
Differentiate between the regression and classification supervised learning models
Compare different ML model types and architectures, from no code to low code to custom training
Design, implement, and tune ML models
Export data to a GitHub repository for data management and governance
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