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,...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
2023
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| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | DE-188 |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 Online-Ressource (xiv, 312 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781098146795 9781098146788 |
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Datensatz im Suchindex
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| discipline | Informatik |
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| index_date | 2024-07-03T23:00:16Z |
| indexdate | 2024-07-20T06:23:03Z |
| institution | BVB |
| isbn | 9781098146795 9781098146788 |
| language | English |
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| spelling | Stripling, Gwendolyn Verfasser aut Low-code AI a practical project-driven introduction to machine learning Gwendolyn Stripling, PhD & Michael Abel, PhD First edition Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly 2023 1 Online-Ressource (xiv, 312 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier 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. Machine learning Deep learning (DE-588)1135597375 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Artificial intelligence Maschinelles Lernen (DE-588)4193754-5 s Deep learning (DE-588)1135597375 s DE-188 Abel, Michael Verfasser aut Erscheint auch als Druck-Ausgabe 978-1-09-814682-5 |
| spellingShingle | Stripling, Gwendolyn Abel, Michael Low-code AI a practical project-driven introduction to machine learning Machine learning Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4193754-5 |
| title | Low-code AI a practical project-driven introduction to machine learning |
| title_auth | Low-code AI a practical project-driven introduction to machine learning |
| title_exact_search | Low-code AI a practical project-driven introduction to machine learning |
| title_exact_search_txtP | Low-code AI a practical project-driven introduction to machine learning |
| title_full | Low-code AI a practical project-driven introduction to machine learning Gwendolyn Stripling, PhD & Michael Abel, PhD |
| title_fullStr | Low-code AI a practical project-driven introduction to machine learning Gwendolyn Stripling, PhD & Michael Abel, PhD |
| title_full_unstemmed | Low-code AI a practical project-driven introduction to machine learning Gwendolyn Stripling, PhD & Michael Abel, PhD |
| title_short | Low-code AI |
| title_sort | low code ai a practical project driven introduction to machine learning |
| title_sub | a practical project-driven introduction to machine learning |
| topic | Machine learning Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine learning Deep learning Maschinelles Lernen |
| work_keys_str_mv | AT striplinggwendolyn lowcodeaiapracticalprojectdrivenintroductiontomachinelearning AT abelmichael lowcodeaiapracticalprojectdrivenintroductiontomachinelearning |