Machine Learning Foundations

Machine Learning is no longer just for data scientists — it is a core capability for professionals who shape products, strategies, and decisions. Machine Learning Foundations is a mentor-led, project-based programme designed for working adults in Singapore who want to go beyond theory and become genuinely ML-capable in just four weeks. Guided by a 15+ year AI & Machine Learning practitioner with global industry and teaching experience, you will learn how real ML models are built, evaluated, and applied in real business contexts — not toy examples or academic exercises. Through live sessions, hands-on Python and scikit-learn work, and a guided capstone project, this course equips you with the confidence to frame problems correctly, make informed ML decisions, and explain results clearly to technical and non-technical stakeholders. This is not a certification-chasing course — it is for professionals who want practical machine learning skills they can immediately apply at work, in product development, or as a serious step toward AI-driven roles.

4.8 (26 rating)
55 students
language
English

  • Build Real Machine Learning Capability — Not Just Theory
  • Turn Business Problems into Deployable ML Solutions
  • Explain ML Decisions with Confidence to Any Stakeholder
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  • 8 Days
    7.00 PM to 10.00 PM
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Trainer Introduction

Iván Palomares Carrascosa is an experienced AI & Machine Learning practitioner, mentor, and educator with over 15 years of industry and teaching experience. He is the Lead Content Writer at KDnuggets and Machine Learning Mastery, and a global instructor for companies, business schools, and leading MOOCs. Certified in AWS and Google Cloud Machine Learning, Iván is known for helping professionals build practical, real-world ML capability — not just academic knowledge.

course learning outcome

  • Build End-to-End Machine Learning Models
  • tik icon Develop, train, and evaluate real machine learning models using Python and scikit-learn, from data preparation to model validation.
  • Make Informed ML Decisions and Trade-offs
  • tik icon Understand model performance, limitations, and trade-offs to choose the right approach for real-world use cases — not just the “best score.”
  • Frame the Right Problems for Machine Learning
  • tik icon Learn how to translate real business or workplace challenges into ML-ready problem statements with appropriate objectives and constraints.
  • Communicate ML Results with Confidence
  • tik icon Explain model outputs, assumptions, and impact clearly to product, business, and non-technical stakeholders.

KNOWLEDGE YOU WILL GAIN

Machine Learning Concepts
Supervised Learning Techniques
Model Training & Evaluation
Feature Engineering Basics
Python Fundamentals for Machine Learning
scikit-learn Workflow
ML Metrics & Performance Analysis

Course Fees and DURATION

Course Timings: Every Tuesday & Thursday: 7pm – 10pm (4 weeks period)

Full Course Fee: $1080

Early Bird Discount Fee: $880

Entry Requirement

  • tik icon No prior AI, data science, or programming experience is required
  • tik icon Basic understanding of product, business, or operational workflows is helpful but not mandatory
  • tik icon Suitable for product managers, aspiring PMs, career switchers, business owners, and professionals seeking data-driven decision skills
  • tik icon Participants should be comfortable using digital tools and open to hands-on learning

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