This course provides a comprehensive introduction to Artificial Intelligence (AI), covering fundamental concepts, applications of Machine Learning (ML), data understanding, Python programming for AI, human-AI relationships, calculus foundations, Convolutional Neural Networks (CNNs) for feature extraction, and reinforcement learning. Participants will explore AI's impact on various industries, its current capabilities, and future potentials.

Intended audience

AI Engineer, Machine Learning Engineer, Data Scientist, AI Developer, Software Developer, Automation Engineer, Researcher and Students interested in Artificial Intelligence

Prerequisites

Any Diploma or Degree

Assessment & certification

Assessment fee: Included — no extra fee
Assessment mode: Online

NCrF level: 6 (NCrF credit-eligible)

Syllabus

Week 1: Introduction to AI Fundamentals and Historical Context
Week 2: Overview of AI's foundational concepts and historical development.
Week 3: Exploration of AI branches such as machine learning, natural language processing, and robotics.
Week 4: Case studies demonstrating AI applications in healthcare, finance, and autonomous systems.
Week 5: Discussion on AI's role in shaping future technological advancements.
Week 6: Machine Learning Applications
Week 7: Practical applications of supervised learning: classification and regression tasks.
Week 8: Utilization of unsupervised learning techniques: clustering and dimensionality reduction.
Week 9: Implementation of reinforcement learning algorithms for decision-making processes.
Week 10: Evaluation metrics for assessing model performance and selecting appropriate models.
Week 11: Data Understanding, Preprocessing, and Python Programming for AI
Week 12: Techniques for data understanding: data exploration and visualization.
Week 13: Strategies for data preprocessing: cleaning, normalization, and feature extraction.
Week 14: Essential Python programming skills for AI development: syntax, data structures, and libraries (e.g., NumPy, Pandas).
Week 15: Hands-on exercises integrating Python with AI applications: building classifiers, clustering algorithms, and reinforcement learning agents.
Week 16: Ethics and Human-AI Interaction
Week 17: Ethical considerations in AI development: bias, fairness, transparency, and accountability.
Week 18: Impact of AI on society and human-AI collaboration: workforce transformation and ethical AI adoption.
Week 19: Regulatory frameworks and guidelines governing AI technologies: privacy, security, and legal implications.
Week 20: Strategies for mitigating bias in AI algorithms: ethical AI design principles and responsible AI practices.