- 提示:
- 課程詳情以機構網站為準。
課程資料
時間表*
- 2026年11月16日(週一) 上午 9:30 - 下午 5:00
報名日期*
2026年6月23日(週二) - 2026年11月15日(週日)
價錢
HKD 4,800
(Early Bird HK4800
Standard HK6000)
(Early Bird HK4800
Standard HK6000)
課程級別
學習模式
修業期
1 日
教學語言
廣東話
地點
2/F, Centre Point, 181 Gloucester Road, Wanchai, HK
證書
- 可獲發
- (證書的發放與分配，依課程提供者的政策及安排而定。)
*提示: 以上時間表僅供參考，以機構網站為準。
課程簡介
Course description
You will learn how to solve a real-world use case with Machine Learning (ML) and produce actionable
results using Amazon SageMaker. This course walks through the stages of a typical data science process for
Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering.
Individuals will also learn practical aspects of model building, training, tuning, and deployment with
Amazon SageMaker. Real life use case includes customer retention analysis to inform customer loyalty
programs.
- Course level: Intermediate
- Duration: 1 day
Activities
- This course includes presentations, group exercises, and hands-on labs.
Course objectives
In this course, you will:
Prepare a dataset for training
Train and evaluate a Machine Learning model
Automatically tune a Machine Learning model
Prepare a Machine Learning model for production
Think critically about Machine Learning model results
課程內容
Module 1: Introduction to machine learning
Types of ML
Job Roles in ML
Steps in the ML pipeline
Module 2: Introduction to data prep and SageMaker
Training and test dataset defined
Introduction to SageMaker
Demonstration: SageMaker console
Demonstration: Launching a Jupyter notebook
Module 3: Problem formulation and dataset preparation
Business challenge: Customer churn
Review customer churn dataset
Module 4: Data analysis and visualization
Demonstration: Loading and visualizing your dataset
Exercise 1: Relating features to target variables
Exercise 2: Relationships between attributes
Demonstration: Cleaning the data
Module 5: Training and evaluating a model
Types of algorithms
XGBoost and SageMaker
Demonstration: Training the data
Exercise 3: Finishing the estimator definition
Exercise 4: Setting hyper parameters
Exercise 5: Deploying the model
Demonstration: hyper parameter tuning with SageMaker
Demonstration: Evaluating model performance
Module 6: Automatically tune a model
Automatic hyper parameter tuning with SageMaker
Exercises 6-9: Tuning jobs
