Course Overview
Python stands as the indispensable engine for modern Artificial Intelligence and Machine Learning initiatives, driving innovation, strategic foresight, and operational efficiency in the global marketplace. Its accessible syntax, robust suite of specialized libraries, and scalable ecosystem provide a critical competitive advantage, enabling organizations to rapidly transform data into actionable intelligence.
This intensive 3-day executive program is engineered to transition professionals from core Python competencies to the construction, assessment, and deployment readiness of foundational AI/ML models, with a strategic focus on commercial application, enterprise-grade data, and production-level implementation.
Python for AI & ML: Foundations to Deployment is a results-driven executive workshop designed to cultivate high-value Python capabilities for data intelligence, predictive modeling, and deployment pipeline awareness.
Participants will master essential programming constructs, leverage industry-standard tools for data wrangling and analysis, develop and validate machine learning models, and gain critical insights into operationalising AI/ML solutions. The curriculum is meticulously structured to merge strategic understanding with intensive, practical execution, ensuring immediate ROI through applicable, production-oriented skills.
Learning Objectives
Upon completion, participants will be equipped to:
• Leverage Python’s core architecture for developing AI/ML-driven business solutions.
• Utilize key Python libraries to manage, interrogate, and derive insight from enterprise data assets.
• Execute data pre-processing and visualization to inform strategic decision-making.
• Construct, optimize, and validate predictive models to address specific business challenges
• Architect deployment roadmaps and integrate models into broader business workflows.
• Translate AI/ML concepts into tangible business applications and value propositions.
Learning Outcomes
Participants will demonstrate competency to:
• Develop production-grade Python code aligned with business objectives.
• Drive data-centric decision-making through proficient dataset manipulation and analysis.
• Deploy select machine learning algorithms to solve operational and strategic problems.
• Quantify model efficacy and ROI using key performance metrics and validation frameworks.
• Navigate model deployment lifecycles, from development to integration and monitoring.
• Articulate AI/ML project outcomes, insights, and business impact to stakeholders.
Course Code: TGS-2025061179
Course Duration: 3 days
Course Fee: $1800.00
| TYPE | Singapore Citizens and
Singapore Permanent Residents |
Employer-sponsored and self-sponsored Singapore Citizens aged 40 years old and above | SME-sponsored
Local employees (i.e Singapore Citizens and Singapore Permanent Residents) |
| SkillsFuture
Funding (Baseline) |
SkillsFuture
Mid-career Enhanced Subsidy |
SkillsFuture
Enhanced Training Support For SMEs |
|
| Course Fee | $1800.00 | $1800.00 | $1800.00 |
| SkillsFuture Funding | $900.00 | $1260.00 | $1260.00 |
| Total Nett Fee | $900.00 | $540.00 | $540.00 |
| GST (9% of Course fee) | $162.00 | $162.00 | $162.00 |
| Total Fee Payable to | $1062.00 | $702.00 | $702.00 |
Price indicated are the payable amount with GST, must be made before the commencement of the course.
-
Payment by Cash/Paynow/SkillsFuture Credit (for eligible Singaporeans), cheque or Giro Bank Transfers (for companies).
-
Refund of course fees is applicable upon absence/withdrawal with a valid reason (e.g. production of mc)
*For more information on Course fee funding, please visit this link: https://www.myskillsfuture.gov.sg/content/portal/en/career-resources/career-resources/education-career-personal-development/skillsfuture-funding-changes.html
Absentee Payroll Funding Support is applicable for Employer sponsored SGs or PRs; standardised at $4.50/hr.
For more information go to: https://www.enterprisejobskills.gov.sg/content/upgrade-skills/course-fee-and-absentee-payroll-funding.html
DAY 1: Python Foundations & Data Handling (8 Hours)
Topics Covered
- Introduction to Python and AI/ML ecosystem
- Python syntax, variables, and data types
- Control structures (loops, conditionals)
- Functions and modules
- Working with NumPy arrays
- Data handling with Pandas
- Reading and writing datasets
Hands-on Labs
- Writing Python scripts
- Data loading and exploration
- Basic data manipulation exercises
Key Skills Developed
- Python fundamentals
- Structured programming
- Data handling readiness
DAY 2: Data Analysis & Machine Learning Fundamentals (8 Hours)
Topics Covered
- Exploratory Data Analysis (EDA)
- Data visualization techniques
- Data cleaning and preprocessing
- Introduction to Machine Learning
- Supervised learning concepts
- Regression and classification models
- Model training and testing
Hands-on Labs
- EDA on real datasets
- Building regression and classification models
- Evaluating models using metrics
Key Skills Developed
- Analytical thinking
- ML model development
- Performance evaluation
DAY 3: Model Deployment & Applied Use Cases (8 Hours)
Topics Covered
- Model optimization and validation
- Overfitting and underfitting
- Model persistence (saving/loading models)
- Introduction to deployment concepts
- APIs for ML models (Flask / FastAPI overview)
- End-to-end AI/ML workflow
- Industry use cases and best practices
Hands-on Labs
- End-to-end mini project
- Model deployment simulation
- Interpreting and presenting results
Key Skills Developed
- End-to-end AI/ML workflow understanding
- Deployment readiness
- Practical problem-solving
Assessment & Evaluation
|
Assessment Component |
Description |
|
Knowledge Checks |
Python & ML concepts |
|
Hands-on Exercises |
Lab-based coding tasks |
|
Mini Project |
End-to-end ML workflow |
|
Final Review |
Model results & interpretation |
Target Audience
- Students and graduates
• Working professionals transitioning into AI/ML
• Data analysts and software engineers
• Business and technology managers
• Educators and trainers
Career & Productivity Impact
Relevant Roles
• Junior Data Analyst
• ML Engineer (Entry-level)
• AI/ML Associate
• Python Developer
Productivity Gains
• Faster data analysis
• Automated modeling workflows
• Improved decision support
• Reduced dependency on manual analysis
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