Synopsis
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.
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
"synopsis" may belong to another edition of this title.
About the Author
- Gwendolyn 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". Gwendolyn is also the Author of the LinkedIn Learning course: Artificial Intelligence Foundations: Neural Networks (released 9/18/2023) and Advanced NLP with Python for Machine Learning (coming in 2024).
- 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.
From the Inside Flap
Everyone from those in tech-adjacent roles to aspiring data scientists and
ML engineers can benefit from the project-based approach and no-code and low-code
solutions presented in this wonderfully written book.
—Eric Pilotte, Global Head of Technical and Business Training Delivery, Google Cloud
Low-Code AI is what I have been looking for to help jumpstart learning AI. This book provides an easy-to-follow guide that helps those of us who want to harness the power of AI for data driven decision making, but that do not yet have years of ML coding experience. I am grateful for this highly accessible book and feel seen!
—Shana Rigelhaupt, Product Manager, The Carey Group and Aspiring Citizen Data Scientist
Low-Code AI is a very special book that manages to strike the right balance
between practical low-code recipes to get started with ML and in-depth explanations
that are accessible to beginners. A great read to start a journey in AI from scratch and build quality intuition in this always-changing field.
—Benoit Dherin, ML engineer, Google Cloud
Whether you are familiar with coding or are a beginner, this excellent and detailed guide unlocks the potential of ML, illustrated through real-world use cases and hands-on problems
—Michael Munn, Research software engineer, Google Cloud
"About this title" may belong to another edition of this title.
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Paperback. Condition: new. Paperback. 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" 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781098146825