Low-Code AI: A Practical Project-Driven Introduction to Machine Learning

by Gwendolyn Stripling, Michael Abel

Overview

Take a data-first and use-case driven approach to understanding machine learning and deep learning concepts with Low-Code AI. This hands-on guide presents three problem-focused ways to learn ML: no code using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. You'll 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.

You'll learn how to:

• Distinguish structured and unstructured data and understand the different 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 machine learning 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

Product Details

ISBN-13:	9781098146825
Publisher:	O'Reilly Media, Incorporated
Publication date:	10/17/2023
Pages:	325
Product dimensions:	7.00(w) x 9.19(h) x 0.69(d)

About the Author

Michael Abel, PhD, is the technical lead for the specialized training program at Google Cloud, working to accelerate and deepen Cloud proficiency of customers through differentiated and non-standard learning experiences. Formerly, Abel was a data and machine learning technical trainer at Google Cloud and has taught the following Google Cloud courses: "Machine Learning on Google Cloud," "Advanced Solutions Labs ML Immersion," and "Data Engineering on Google Cloud." Before joining Google, Abel served as a Visiting Assistant Professor of Mathematics at Duke University, where he performed mathematics research and taught undergraduate mathematics.

Michael Abel is the technical lead for the specialized training program at Google Cloud, working to accelerate and deepen Cloud proficiency of customers through differentiated and non-standard learning experiences. Formerly, Abel was a data and machine learning technical trainer at Google Cloud and has taught the following Google Cloud courses: Machine Learning on Google Cloud, Advanced Solutions Labs ML Immersion, and Data Engineering on Google Cloud. Before joining Google, Abel served as a Visiting Assistant Professor of Mathematics at Duke University, where he performed mathematics research and taught undergraduate mathematics.