000			01986 a2200181 4500
008			231222b |||||||| |||| 00| 0 eng d
020			_a9789355425560
082			_a005.74015 _bSTR
100			_aStripling Gwendolyn
245			_aLow-Code AI: A Practical Project-Driven Introduction to Machine Learning
250			_a1st
260			_bShroff/O’Reilly _c2023 _aIndia
300			_a328
520			_aTake 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 Source: https://www.amazon.in/Low-Code-Practical-Project-Driven-Introduction-Grayscale/dp/9355425562/ref=sr_1_1?crid=3AYZ4GS9OSL8W&keywords=9789355425560&qid=1703238978&sprefix=9789355423498%2Caps%2C364&sr=8-1
700			_aAbel Michael
906			_aBusiness Analytics
942			_c1 _2ddc
999			_c98177 _d98177