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 Authors
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.
"About this title" may belong to another edition of this title.
Search results for Low-Code AI: A Practical Project-Driven Introduction...
Stock Image
Low-Code AI: A Practical Project-Driven Introduction to Machine Learning
Published by
O'Reilly Media (edition 1), 2023
ISBN 10: 1098146824
ISBN 13: 9781098146825
Used
Paperback
Seller: BooksRun, Philadelphia, PA, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Seller Inventory # 1098146824-8-1
Stock Image
Low-Code AI: A Practical Project-Driven Introduction to Machine Learning
Seller: HPB-Red, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! Seller Inventory # S_426187623
Stock Image
Low-Code AI: A Practical Project-Driven Introduction to Machine Learning
Seller: ThriftBooks-Dallas, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: Fair. No Jacket. Former library book; Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less. Seller Inventory # G1098146824I5N10
Stock Image
Low-Code AI: A Practical Project-Driven Introduction to Machine Learning [Paperback] Stripling, Gwendolyn and Abel, Michael
Seller: Lakeside Books, Benton Harbor, MI, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books! Seller Inventory # OTF-S-9781098146825
Seller Image
Low-Code AI : A Practical Project-Driven Introduction to Machine Learning
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 45802689-n
Stock Image
Low-Code AI
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # WO-9781098146825
Stock Image
Low-Code AI
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # WO-9781098146825
Buy New
£ 42.63
£ 5.02 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 15 available
Seller Image
Low-Code AI : A Practical Project-Driven Introduction to Machine Learning
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition. Seller Inventory # 45802689
Stock Image
Low-Code AI: A Practical Project-Driven Introduction to Machine Learning
Seller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # I-9781098146825
Stock Image
Low-Code AI (Paperback)
Published by
O'Reilly Media, Sebastopol, 2023
ISBN 10: 1098146824
ISBN 13: 9781098146825
New
Paperback
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Seller rating 5 out of 5 stars
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