
Generative AI learns from vast data sets with neural networks to create new images, text, and music in learned styles.
Discover how natural language processing helps computers understand spoken and written language, powering chatbots, translation, sentiment analysis, and search. Learn tokenization, tagging, embeddings, context models, and ethical considerations.
Explore a concrete natural language processing workflow using movie reviews to label sentiment. Preprocess text, convert words to features with bag of words, train a logistic regression classifier, and evaluate.
Deep learning stacks neural network layers to learn features from data. Train with backpropagation and activation functions to power voice assistants, self-driving cars, and medical imaging tasks.
Discover how generative AI creates new content from images to music by learning patterns from large training data.
Explore large language models trained on massive text to predict next words and generate coherent writing, coding help, and research summaries.
Create a custom GPT in ChatGPT by configuring its name, description, and instructions to include details, images, and a timeline; then publish to the GPT store and try different models.
Explore how generative ai can create stories, art, and voices while prioritizing ethics, verification, and transparency to prevent deepfakes, misinformation, and privacy breaches.
This comprehensive course provides an engaging introduction to the exciting world of Artificial Intelligence (AI) for learners who want both a foundational understanding and hands-on exposure. We begin by demystifying AI, exploring its history, core principles, and how modern machine learning techniques allow machines to learn and adapt. Next, we dive into Natural Language Processing (NLP) to see how computers interpret human language for tasks such as text analysis and chatbots. The journey continues with an overview of deep learning, where neural networks extract patterns from large datasets to accomplish tasks ranging from image recognition to predictive analytics. Along the way, students gain a big-picture view of how AI has evolved—from simple rule-based systems to today’s generative models that create entirely new pieces of content on their own.
The course then focuses on how generative AI works under the hood, giving learners a chance to explore Large Language Models (LLMs) such as GPT-style systems and popular image-generation models. Through demonstrations and guided practice, participants discover how these advanced models produce text, images, or other media based on their training. We discuss the ethical considerations and responsible use of AI, equipping students to identify potential misuse and maintain best practices regarding bias, privacy, and transparency. Finally, we introduce a simple path to building a custom GPT solution—providing a step-by-step roadmap that enables enthusiasts to create specialized Q&A or creative applications without writing code. By the end of the course, learners will feel confident discussing AI fundamentals, applying generative models, and critically evaluating AI’s impact on society.