Description
Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, youll learn key ML concepts by using real-world datasets with realistic problems.
Business 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.
Youll 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
Author: Gwendolyn Stripling, Michael Abel
Binding Type: Paperback
Publisher: OReilly Media
Published: 10/17/2023
Pages: 325
Weight: 1.15lbs
Size: 9.19h x 7.00w x 0.69d
ISBN: 9781098146825
Language: English
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