Low-Code AI
eBook Details:
- Paperback: 325 pages
- Publisher: WOW! eBook (October 17, 2023)
- Language: English
- ISBN-10: 1098146824
- ISBN-13: 978-1098146825
eBook Description:
Low-Code AI: A Practical Project-Driven Introduction to Machine Learning
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 by using real-world datasets with realistic problems.
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
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.
