Low-Code AI

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

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.


Leave a Reply

Your email address will not be published. Required fields are marked *