Machine Learning Series

Build understanding that lasts.

Build a deep and connected understanding of Machine Learning — from fundamental concepts and traditional algorithms to Deep Learning, CNNs, RNNs, Transformers, Generative AI, and other modern approaches. Rather than simply using libraries and calling model.fit(), this course focuses on understanding how and why models work. Each topic is explored through intuition, mathematics, visual explanations, internal model behaviour, step-by-step Python implementation, evaluation, and practical applications. Participants will also work on hands-on exercises and at least one practical project designed to connect theory with real-world Machine Learning development. Model Evaluation and Explainable AI are integrated throughout the course.

What you’ll learn

• Understand the intuition and mathematics behind major Machine Learning algorithms
• Explain how models learn, make predictions, and optimize their parameters
• Implement Machine Learning and Deep Learning models using Python
• Connect mathematical concepts with practical model behaviour
• Visualize model behaviour, features, decision boundaries, and learning processes
• Evaluate models using appropriate metrics and validation strategies
• Interpret model predictions using Explainable AI techniques
• Understand CNNs, RNNs, LSTMs, GRUs, Transformers, and Large Language Models
• Explore Autoencoders, Transfer Learning, Generative AI, Few-Shot Learning, Active Learning, and Reinforcement Learning
• Design experiments and critically interpret Machine Learning results
• Build and present a practical Machine Learning project
• Develop the foundation needed for further study, research, interviews, and real-world AI projects

Your learning roadmap

Before you begin

• Basic Python programming knowledge
• Familiarity with variables, loops, functions, lists, and basic Python syntax
• Basic mathematical understanding is helpful, but advanced mathematics is not required
• No previous Machine Learning experience is required