This Artificial Intelligence Fundamentals Level 2 course consists of six structured lectures, followed by an assessment, each designed to introduce a key area of artificial intelligence knowledge.
Lecture one introduces artificial intelligence, defining AI, exploring its historical development, reviewing real-world applications, and introducing Python as a foundational programming language used in AI contexts.
Lecture two focuses on machine learning, explaining supervised and unsupervised learning approaches. Learners gain conceptual understanding of linear regression, classification algorithms, and clustering techniques such as k-means and hierarchical clustering.
Lecture three introduces neural networks, covering basic neural network structures, feedforward networks, convolutional neural networks, recurrent neural networks, and the concept of deep learning and its applications.
Lecture four explores natural language processing, including text pre-processing, word embeddings such as Word2Vec and GloVe, neural networks used in NLP, and applications such as sentiment analysis and text classification.
Lecture five introduces computer vision, covering image pre-processing, convolutional neural networks for vision tasks, object detection and recognition, and image segmentation concepts.
Lecture six examines the ethics and societal implications of artificial intelligence, addressing topics such as AI and employment, bias and fairness, privacy concerns, and ethical considerations in AI research and development.
The assessment evaluates learners’ understanding of artificial intelligence concepts, terminology, applications, and ethical awareness.
