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AI and ML Fundamentals
From perceptrons to transformers: a practical introduction to artificial intelligence and machine learning for engineers.
Beginner1.1 hours10 lessons
Start Course โWhat You'll Learn
- โDistinguish between AI, machine learning, deep learning, and generative AI
- โUnderstand the three main types of machine learning: supervised, unsupervised, and reinforcement
- โWalk through the end-to-end ML lifecycle from problem framing to deployment and monitoring
- โRecognise common algorithms: linear regression, decision trees, k-means, and gradient boosting
- โExplain how neural networks learn through forward and backward propagation
- โUnderstand the transformer architecture that powers modern large language models
- โApply prompt engineering techniques: zero-shot, few-shot, chain-of-thought, and RAG
- โIdentify ethical risks in AI: bias, hallucination, privacy, and copyright
- โMap AI/ML services across AWS (SageMaker, Bedrock), Azure (AI Foundry), and Google Cloud (Vertex AI)
- โChoose the right tool for an AI use case: build, fine-tune, or call a managed API
Prerequisites
- โขBasic programming familiarity (Python is a plus but not required for this course)
- โขHigh school level mathematics โ no calculus or linear algebra required
Course Curriculum
Module 1: AI and ML Foundations
Module 2: The Machine Learning Lifecycle
Module 3: Algorithms and Models
Module 4: Generative AI and LLMs
Module 5: Operating AI in the Real World
Practice for the Real Exam
After completing this course, test yourself with exam-style practice questions.