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How to Develop an AI-Based Personalized Service: Recommending the Best Workout Time for Me

Processing actual smart health data held by Daegu Haany University for use in machine learning, and creating customized smart health applications using vibe coding and refined information.

6 learners are taking this course

Level Beginner

Course period Unlimited

AI
AI
Vibe Coding
Vibe Coding
AI
AI
Vibe Coding
Vibe Coding
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What you will gain after the course

  • Understanding the Basics of Artificial Intelligence

  • Basic Understanding of Smart Health Data

  • Application development using vibe coding

  • Understanding the Legal Nature of Health Data

How to Develop AI-Powered Personalized Services: Recommending the Right Exercise Time for Me

After taking the course, you’ll be able to create the following

Health Data Monitoring Dashboard

Personalized exercise time recommendation model

My own workout time recommendation app

AI Code Review and App Improvement

  • What will you understand and be able to do after taking the course?


  • It explains as specifically as possible how participants can change.

What You’ll Learn

Read health data with AI and create your own personalized service

Can the health data accumulated on a smartwatch be turned into a service tailored to my needs? In this course, you will learn step by step, from the basic concepts of artificial intelligence to understanding smart health data, developing personalized recommendation models, and creating applications. Using the topic “Recommending the Right Exercise Time for Me,” experience the process of data passing through an AI model and becoming a complete service.

Check the quality of the collected data and use TensorFlow to build a personalized healthcare model. Then, create and update an application that displays recommendation results through vibe coding. You will also explore methods for solving problems through AI-assisted code review and multi-agent discussions.

This six-session course covers the legal characteristics of health data, the responsibilities and limitations of AI recommendations, and the differences between wellness services and medical devices. It is designed to help you understand not only model development and application creation, but also the considerations involved in turning them into real-world services.

After completing the course, you’ll be able to create the following:


1. Health Data Monitoring Dashboard

You can explore the types of data that can be collected from smart health devices and create an inspection screen to check the condition of the data, including missing values and outliers. Through AI-powered data inspection, you will learn how to review data quality before developing a model.

2. Personalized Exercise Time Recommendation Model

You can create a model that recommends exercise times tailored to each individual based on health data. You will check your Python execution environment and computer specifications, then learn the process from preparing data to modeling and reviewing prediction results using TensorFlow.

3. My Personalized Workout Time Recommendation App

You can create an application that displays recommendation results when users enter information. Using vibe coding, you will design the input and results screens and experience the process of creating a service connected to an AI model.

4. AI Code Inspection and App Improvement

You can create an AI-based code inspection program and check for common errors that can occur during data processing. You will apply improvements to the application and review directions for enhancing the service based on considerations related to health data and AI recommendations.

※ The images are examples of learning outcomes created based on the curriculum. They may differ from the actual hands-on screens and completed results in the course. The exercise time shown in the images is a sample value intended to demonstrate the screen layout.

Notes Before Enrollment

  • Please provide detailed notes on anything students should know for taking the course.

  • This helps students fully understand the topics covered in the course and increases their learning satisfaction.

Practice Environment

  • Operating system and version (OS): OS type and version, such as Windows, macOS, Linux, Ubuntu, Android, and iOS

  • Tools used: Software/hardware versions and pricing plans required for the practical exercises, whether virtual machines are used, etc.

  • PC specifications: Recommended requirements for running the program, including the CPU, memory, disk, graphics card, etc.

Learning materials

  • Format of the learning materials provided (PPT, cloud links, text, source code, assets, programs, example problems, etc.)

  • Quantity and file size, as well as features and notes regarding other learning materialsғыҙ

Prerequisite Knowledge and Notes

  • Required prior knowledge, taking the learning difficulty into account

  • Content directly related to taking the course, such as lecture video quality (audio/video), and recommended study methods

  • Information regarding questions/answers and future updates

  • Notices regarding the copyright of lectures and learning materials

Recommended for
these people

Who is this course right for?

  • Students without a background in computer science or artificial intelligence and no experience with vibe coding

  • Computer science and artificial intelligence students who are not majoring in these fields but are interested in smart health data and medical data

Need to know before starting?

  • We use generative artificial intelligence models in class.

  • We handle sensitive data related to smart healthcare and medicine.

Hello
This is aisw

434

Learners

17

Reviews

4.9

Rating

5

Courses

Pukyong National University Software Convergence Innovation Institute

Curriculum

All

6 lectures ∙ (3hr 4min)

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