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Lowcode Ai: A Practical Projectdriven Introduction To Machine Learning,Used
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Take a datafirst and usecasedriven approach with LowCode AI to understand machine learning and deep learning concepts. This handson guide presents three problemfocused ways to learn nocode ML using AutoML, lowcode using BigQuery ML, and custom code using scikitlearn and Keras. In each case, you'll learn key ML concepts by using realworld datasets with realistic problems.Business and data analysts get a projectbased introduction to ML/AI using a detailed, datadriven 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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