Machine Learning Foundations

Machine learning education is everywhere. Workplace-ready, deployable capability is not. Most learners aren’t short on information — they’re short on a coherent pathway. Fragmented tutorials create isolated skills, thin project narratives, and uncertainty when it’s time to justify choices to stakeholders.

Machine Learning Foundations is a practitioner-led, outcomes-driven course built to close that gap. You’ll learn to translate a real workplace problem into a clearly scoped ML initiative, define defensible success criteria, and assemble a credible first deployment pathway — reaching Pilot-Level readiness with a project package you can stand behind.

language
English

  • Build real ML models end-to-end using Python + scikit-learn.
  • Evaluate models, interpret results, and explain outcomes to non-technical stakeholders.
  • Turn a workplace problem into a guided, well-formulated ML capstone project
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  • 4 Weeks | Online Synchronous
    7.00 PM to 10.00 PM
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Trainer Profile

Outcomes & Deliverables

This course is for:

  • IT practitioners moving into applied ML who need a structured pathway to prove capability quickly with a workplace-credible deliverable.
  • Software engineers / AI developers delivering an ML pilot, who want a scoping + evaluation + deployment-pathway framework to avoid building the wrong thing and get stakeholder approval faster.

     

You’ll also receive:

  • ML Project Blueprint (use case → target variable → required data → success metrics → key risks)
  • Evaluation Design (baseline + metric justification + error trade-offs + what conclusions are valid)
  • Pilot-Level Deployment Pathway Outline

Curriculum-at-a-Glance

  1. Machine Learning Introduction
    A crisp orientation to the ML landscape: data analysis foundations, core AI concepts, and how machine learning fits into an end-to-end lifecycle—grounded in the Python toolchain used in practice.
    (Introduction to Data Analysis and Data Science | Introduction to AI: history and basic concepts | ML definition, concepts, and lifecycle | Python libraries and tools for data science and ML)

  2. Hands-on Machine Learning
    Build capability through application: prepare data, train supervised models, evaluate performance with the right techniques, and complete a full end-to-end project with exposure to high-signal use cases.
    (Data preprocessing and visualization | Supervised learning: regression and classification | Evaluation of ML models | Cross-validation and hyper-parameter tuning | Advanced predictive models | Unsupervised models | Full, end-to-end ML project | Salient applications: recommender systems )

  3. Advanced Machine Learning
    A guided view of modern ML directions—deep learning, computer vision, and generative/LLM applications—plus what it means to deploy models in real settings, supported by demos. 
    (Deep learning foundations | Computer vision techniques and tasks | Generative models and LLMs; using pre-trained LLMs | Deploying AI/ML models in real-world + example demos)

Course Fees and DURATION

Course Schedule (Live Online)
4 Weeks  · Every Monday & Friday 7:00 PM – 10:00 PM (SGT)

Programme Fee
SGD 1,500

Entry Requirement

  • tik icon A basic IT / software development literacy
  • tik icon No strict Python prerequisite, but you should be prepared to learn through hands-on coding in Python, using industry-standard libraries such as pandas, scikit-learn, and TensorFlow
  • tik icon Comfortable working with tabular datasets (e.g., CSV/Excel-style data) and following structured steps for cleaning, analysis, and basic visualisation.
  • tik icon Participants should be comfortable using digital tools and open to hands-on learning

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