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Developing AI applications requires more than theory. It demands programming skill, model evaluation, and real-world system design. This hands-on course is built for IT professionals ready to step into the AI space. Participants will learn Python for AI development, explore LLMs, and gain experience with tools such as LangChain, Hugging Face, and Azure AI Hub. The course focuses on deployment, prompt engineering, and building robust AI systems.
Learning Outcomes:
Analyse the programming structure used in the AI application.
Apply essential principles to optimize algorithms efficiency when designing and developing AI model for specific tasks
Evaluate the strengths and limitations of the data model used in the AI applications
Evaluate the feasibility of the deployed AI data model.
Improve the AI application to ensure its relevance for the organisation.
Key Topics:
Python programming and machine learning foundations
Generative AI tools and security
Prompt engineering for developers
Chatbot development and multimodal AI
LangChain, Hugging Face, and large language models
FUNDING INFORMATION
SkillsFuture Singapore (SSG)
Funding is available on Course Fee. Please see below for the eligibility categories available.
| Self-sponsored | Singapore Citizen & PR aged ≥ 21 years | Up to 50% funding |
| Singapore Citizen aged ≥ 40 years | Up to 70% funding | |
| Company Sponsored (Non-SME) | Singapore Citizen & PR aged ≥ 21 years | Up to 50% funding |
| Singapore Citizen aged ≥ 40 years | Up to 70% funding | |
| Company Sponsored (SME) | Singapore Citizen & PR aged ≥ 21 years | Up to 70% funding |
| Singapore Citizen aged ≥ 40 years | Up to 70% funding |
SSG Funding Requirements
- Trainees must scan their attendance twice daily using the SingPass application.
- Trainees must attain at least 75% attendance.
- Trainees must pass the in-house assessment to be eligible for funding.
- Trainee and/or sponsoring company is/are required to meet all SSG-mandated eligibility criteria and requirements for funding. For more information, please refer to SkillsFuture homepage.
Appeal Policy and Procedure
- As a candidate in this course assessment, you may appeal your results if you disagree with them.
- To do so, submit your written appeal request via email to esv_comat_cse@stengg.com within 3 working days from date of assessment.
Cancellation, Postponement and Refund Policy
- Request for cancellation or postponement must be submitted in writing more than 4 weeks before the class start date to avoid any charges.
- Written notice for cancellation or postponement received 2 to 4 weeks before class start date will incur Late Cancellation Charge - 50% of course fee.
- Written notice for cancellation or postponement received less than 2 weeks before class start date will incur Late Cancellation Charge - 100% of course fee.
- If payment has been made and ST Engineering e-Services Pte Ltd accepts the trainee's written notification to cancel or withdraw from the course, ST Engineering e-Services Pte Ltd will issue a refund, less any applicable Late Cancellation Charges.
Feedback Policy and Procedure
- You may submit feedback via email to esv_comat_cse@stengg.com or your servicing Account Manager.
- Any formal feedback will be handled within 10 working days from receipt with a written reply given. An interim reply will be provided should more time be required.
Module 1 : Introduction To Python Programming
Module 2 : Operators and Variables in Python
Module 3 : Python Datatypes and functions
Module 4: File Handling
Module 5 : Introduction To Machine Learning
Module 6 :Working with NumPy and Pandas
Module 7 : Supervised VS Un-supervised Learning
Module 8 : Linear regression - Multi variables
Module 9 :Analyze multiple algorithms
Module 10: Evaluate decision tree and liner regression
Module 11: Working with Generative AI
Module 12: Generative AI - Security and AI in different sectors
Module 13 : Prompt Engineering Concepts
Module 14 : Prompts for Python Developers
Module 15 : Design and Develop ChatBots (Cricket ChatBot, Web-based ChatBot, PDF ChatBot, Multimodal ChatBot, ChatBot to Interact with Web Data)
Module 16 : Working with LangChain
Module 17 : Working with Natural Language Processing (Text classification, Text summarization and data cleaning)
Module 18 : Large Language Model
Module 19 : Using OpenAI LLM- NLP Tasks
Module 20 : Using Anthopic LLM – NLP Tasks * Anthropic chatbot
Module 21 : Hugging face Concepts
Module 22 : Hugging face NLP Tasks
Module 23 : Finetune a LLM Model
Module 24 : Azure AI Hub
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