Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách Low-Code AI A Practical Project-Driven Introduction to Machine Learning (Sách keo gáy, bìa mềm)
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
Categories:Computers - Artificial Intelligence (AI)
Year:2023
Edition:1
Language:english
Pages:325
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