Low-Code AI: A Practical Project-Driven Introduction to Machine Learning (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

以低代碼人工智慧（Low-Code AI）的方式，採用以數據為先並以使用案例驅動的方法來理解機器學習和深度學習的概念。這本實踐指南提供了三種以問題為導向的學習機器學習的方式：使用自動機器學習（AutoML）的無代碼方法、使用BigQuery ML的低代碼方法，以及使用scikit-learn和Keras的自定義代碼方法。通過使用真實世界的數據集和實際問題，您將學習到關鍵的機器學習概念。

本書針對業務和數據分析師提供了一個基於項目的機器學習/人工智慧（ML/AI）入門，採用詳細的數據驅動方法：載入和分析數據，將數據輸入到機器學習模型中；構建、訓練和測試模型；並將模型部署到生產環境中。作者Michael Abel和Gwendolyn Stripling向您展示了如何為零售、醫療保健、金融服務、能源和電信等領域構建機器學習模型。

您將學習到以下內容：
- 區分結構化和非結構化數據，並了解它們所帶來的不同挑戰
- 可視化和分析數據
- 對輸入機器學習模型的數據進行預處理
- 區分回歸和分類監督學習模型
- 比較不同的機器學習模型類型和架構，從無代碼到低代碼到自定義訓練
- 設計、實施和調整機器學習模型
- 將數據導出到GitHub存儲庫進行數據管理和治理