Da: HPB-Red, Dallas, TX, U.S.A.
paperback. Condizione: 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!
Da: ThriftBooks-Dallas, Dallas, TX, U.S.A.
Paperback. Condizione: Fair. No Jacket. Former library book; Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New.
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition.
Da: California Books, Miami, FL, U.S.A.
EUR 58,02
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 50,13
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 56,81
Quantità: 2 disponibili
Aggiungi al carrelloCondizione: New. In.
Da: BargainBookStores, Grand Rapids, MI, U.S.A.
Paperback or Softback. Condizione: New. Low-Code AI: A Practical Project-Driven Introduction to Machine Learning. Book.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 59,35
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Revaluation Books, Exeter, Regno Unito
EUR 76,78
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 350 pages. 9.19x7.00x0.69 inches. In Stock.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 87,00
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: Neu. Neu Neuware, Importqualität, auf Lager - 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.
Da: Revaluation Books, Exeter, Regno Unito
EUR 71,42
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 350 pages. 9.19x7.00x0.69 inches. In Stock. This item is printed on demand.