Certificate in AI Agent Development for Business Applications
AI 商業智能代理開發專業證書課程

本課程專為初學者設計，即使缺乏程式設計背景，也能輕鬆入門。課程將教您如何結合 Python 和 JavaScript 開發強大的 AI 智能代理，Python 負責後端 AI 邏輯開發，JavaScript 用於構建前端網頁界面。課程分為基礎部分與兩個實戰案例，分別是「會議助手代理」和「發票處理代理」。通過本課程，您將掌握 AI 技術在商業應用中的實際開發與應用能力，助力企業提升效率並推動數位化轉型。

課程特色 ✨

✅ 全方位技能培養： 學習如何結合 Python 和 JavaScript，全面掌握後端 AI 邏輯開發與前端互動界面設計，打造強大的智能代理系統。
✅ 實作應用導向 🤖： 課程包含兩個實戰案例：「會議助手代理」與「發票處理代理」，幫助學員學以致用，解決實際業務問題。
✅ 循序漸進 🪜： 從基礎開始，逐步深入講解，適合零基礎學員，降低學習門檻，快速掌握 AI 開發核心技能。
✅ 技術結合實用 📚： 學習 AI 技術如何應用於數據分析、自動化流程和智能決策，提供實用的商業解決方案。
✅ 靈活學習結構 🎥： 課程提供錄製視頻，學員可反覆播放，按需進行單元學習，確保完全掌握內容。

課程收穫 🎓

💡 AI 智能代理開發能力： 掌握 Python 後端邏輯和 JavaScript 前端界面設計，開發完整的智能代理系統。
⚙️ 實戰案例經驗： 通過會議助手和發票處理案例，學會將技術應用於解決實際業務需求。
🤖 機器學習應用能力： 運用 Python 開發和訓練機器學習模型，提升數據分析與預測能力。
📱 全端開發技能： 學習如何構建互動式前端界面，與後端無縫集成，實現用戶友好的多功能應用。
🎨 創新實現能力： 專注於創新設計，開發如智能助手、數據處理工具等高效的業務應用。

完成課程後，您將具備：

全方位技能： 從數據分析到應用程式開發，全面掌握 AI 智能代理開發技術。
實際應用經驗： 綜合專案練習，幫助您將理論轉化為實際技能，應用於商業場景。
強化職場競爭力： 掌握技術驅動的核心能力，成為未來辦公室的專家。

課程目標對象 👩‍💻👨‍💻

🔰 初學者與職場人士： 對 AI 智能代理開發感興趣，或希望掌握 Python 和 JavaScript 技能的學員。
💡 創業者與技術愛好者： 希望利用 AI 技術開發創新應用、實現業務增長的創新型人才。
🏆 管理者與業務領導： 希望通過技術優化業務流程、提升效率並實現智能決策的管理者或主管。

為什麼選擇這課程？ 🤔

🚀 快速上手： 循序漸進的教學設計，適合零基礎學員，幫助您快速掌握 AI 技術開發能力。
⚙️ 提升效率： 結合理論與實戰案例，學員能快速應用學到的技術於實際業務中，解決問題並提升效率。
👩‍🏫 專業講師指導： 由具備豐富經驗的講師授課，確保學員能夠實戰掌握技能。
🎥 靈活學習方式： 提供課程錄製視頻，隨時隨地學習，無限次回放，確保學員完全掌握課程內容。

你將學到什麼 💡

使用 Python 開發後端 AI 智能代理邏輯。

使用 JavaScript 設計互動式前端界面。

構建會議助手：轉錄音頻並生成會議摘要。

開發發票處理代理：提取發票中的關鍵字段。

集成 MongoDB：存儲和檢索用戶交互和數據。

利用 LLM API，實現對私人文檔的處理與分析。

課程內容

Lesson 1.1: What is an AI Agent?
- Define AI Agents and their essential components (Brain, Perception, Action).
- Understand properties of AI agents: Autonomy, Adaptivity, Proactivity, Reactivity.
- Identify examples of AI agents like chatbots and recommendation systems.
- Understand the roles of Python (AI logic) and JavaScript (web interfaces) in AI agent development.
Lesson 1.2: Setting Up Development Environments
- Set up Python 3.9+, virtual environments (e.g., venv), and essential Python libraries.
- Install Node.js and npm for JavaScript development.
- Gain an overview of frontend tools (HTML, CSS, JavaScript) and optional React setup.
- Configure MongoDB for storing and retrieving agent data.

Lesson 2.1: Python-Based Rule Logic
- Build a simple text-based rule engine using Python.
- Handle user input/output via the command line or API.
- Create an FAQ chatbot with predefined rules.
Lesson 2.2: JavaScript Web Interface
- Build a basic web-based chatbot interface using HTML/CSS for layout and JavaScript for interactivity.
- Connect a Python backend via REST APIs for web interfaces.

Lesson 3.1: Speech-to-Text (Audio Input)
- Use `speech_recognition` in Python for converting speech to text.
- Integrate Google Cloud Speech-to-Text for improved transcription accuracy.
- Utilize the Web Speech API for browser-based speech input in JavaScript.
Lesson 3.2: Text-to-Speech (Audio Output)
- Generate audio responses using `pyttsx3` in Python.
- Use the Web Speech Synthesis API for browser-based voice output in JavaScript.

Lesson 4.1: MongoDB Integration
- Store user interactions and processed data in MongoDB using `pymongo` in Python.
- Retrieve and display data in web interfaces using `mongoose` in JavaScript.
Lesson 4.2: Data Visualization
- Analyze data with `pandas` and visualize it with `matplotlib` in Python.
- Create interactive charts for data analytics on the frontend using Chart.js in JavaScript.

Lesson 5.1: Fetching Live Data
- Fetch data from external APIs (e.g., OpenWeatherMap, News API) using `requests` in Python.
- Fetch API data using `axios` or the Fetch API in JavaScript.
Lesson 5.2: Dynamic Responses
- Combine API data with Python logic to create smarter, context-aware responses.
- Develop a real-time weather or news chatbot as an example.

Lesson 6.1: External LLM Integration
- Integrate with external LLM APIs like OpenAI (using `openai` library) or Google Gemini for NLP tasks in Python.
- Call LLM-based APIs from Node.js backend for web-based interfaces.
Lesson 6.2: Local LLM Deployment
- Set up and run Hugging Face LLM models with `transformers` for local summarization or sentiment analysis in Python.
- Train Hugging Face models on custom, domain-specific datasets in Python.
Lesson 6.3: Fine-Tuning LLMs
- Train Hugging Face models on custom, domain-specific datasets in Python.

Real-Time Audio Transcription: Build a meeting assistant that captures audio in real time and converts it into text using speech-to-text APIs.
AI-Powered Summarization: Implement natural language processing (NLP) techniques to summarize lengthy meeting transcripts into concise, actionable points.
Speaker Identification: Include functionality to identify and differentiate speakers in a conversation for better clarity and organization.
Web Dashboard Integration: Develop an interactive web dashboard using modern front-end frameworks to display the transcription, key summaries, and speaker insights.
Export and Share Features: Add options to export the meeting summary and transcripts in formats like PDF or Word and share them via email or cloud platforms.

Invoice Scanning and OCR: Use Optical Character Recognition (OCR) technology to scan invoices and extract text data from images or PDF files.
Key Field Extraction: Develop an AI model to identify and extract key fields such as invoice number, date, vendor name, item descriptions, quantities, and totals.
Data Validation: Implement logic to validate extracted data, such as checking for missing fields or ensuring numerical fields are correctly formatted.
Web Interface for Review: Create a user-friendly web interface where users can review and edit the extracted data before saving it to a database.
Export and Automation: Enable users to export processed invoice data in formats like Excel or JSON, and integrate with accounting or ERP systems for seamless workflow automation.

導師簡介

Dannis Mok

He has rich experience in business web and apps system development and over 25 years of teaching experience. He has a great passion for learning and teaching new technologies, and his teaching style is clear, to the point, and simplifies complex technologies into easy-to-understand terms.

He has delivered various workshops and classes for well-known corporates, government departments, and local universities, specializing in office automation, data science, data analysis, and business web and apps system development. He is the principal lecturer for NCC Education and University of Greenwich, and has provided training that equips professionals with practical skills tailored to industry needs.

By leveraging his expertise in these areas, he has successfully trained professionals in corporate organizations and government departments to enhance efficiency, adopt data-driven decision-making, and embrace automation technologies.

In addition to his BSc degree in IT, he holds an MBA, an MSc in IT, and an MSc in Telecommunication.

Microsoft Power BI Data Analyst Associate

視像課程內容

了面授課堂，同學亦可重溫課程錄影片段，觀看期為期一年，可在家無限重播。

PowerBI Relationship (08m:59s)

Python Pandas (06:32)

PowerAutomate Auto Sum Up (06:32)

網上學習系統

為配合在職人士的需求，本校的課程已全部錄影，學員可因應自己的學習進度，隨時隨地選擇任何一科開始學習。學員有充裕的時間去不斷重溫及重播相關技術課程片段，務求令自己掌握相關技術。

詳細視像課程內容，請登入網上學習系統觀看。

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報名及付款

Certificate in AI Agent Development for Business Applications

Course Code: AIA2025

Schedule: Starts on 17th Feb (Monday), 7:00 PM – 10:00 PM

Total Duration: 4 lessons

🎉 Summer Discount! Enroll now and save big! 🎉

$3,980 $1,980

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辦公時間: 星期一至星期五上午11時至晚上8時

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Certificate in AI Agent Development for Business Applications
AI 商業智能代理開發專業證書課程

課程特色 ✨