Low-Code AI

Low-Code AITake 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, you'll learn key ML concepts using real world datasets with realistic problems. Business and data analysts get a project based introduction to ML AI using

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, you'll learn key ML concepts 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 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
About the authorGwendolyn Stripling, PhD, is an artificial intelligence and machine learning content developer at Google Cloud, helping learners navigate their generative AI and AI/ML journey. Stripling is the author of the successful YouTube video Introduction to Generative AI� and author of the LinkedIn Learning video Introduction to Neural Networks.� Stripling is an adjunct professor and member of Golden Gate Universitys Masters in Business Analytics Advisory Board. Gwendolyn enjoys speaking on AI/ML, having presented at Dominican University of Californias Barowsky School of Business Analytics, Golden Gate Universitys Ageno School of Business Analytics, Google Cloud NEXT, and Googles Venture Capitalist and Startup program. Gwendolyn is also the Author of the LinkedIn Learning course: Artificial Intelligence Foundations: Neural Networks (released 9/17/2023) and Advanced NLP with Python for Machine Learning (coming in 2024).