Artificial Intelligence

Artificial Intelligence Foundations: Principles is the first part of this course designed as an introduction to artificial intelligence (AI), covering foundational concepts, data handling, and common misconceptions. Learners will explore how AI works through hands-on, no-code experiences in machine learning, generative AI, and automation tools. Real-world applications are highlighted across industries, with case studies and emerging trends shaping the future of AI. Emphasis is also placed on ethical AI practices, including fairness, privacy, and regulatory considerations for responsible use.

Artificial Intelligence Foundations: Practice is the second part of this course designed as an introduction to artificial intelligence (AI), covering foundational concepts, data handling, and common misconceptions. Learners will explore how AI works through hands-on, no-code experiences in machine learning, generative AI, and automation tools. Real-world applications are highlighted across industries, with case studies and emerging trends shaping the future of AI. Emphasis is also placed on ethical AI practices, including fairness, privacy, and regulatory considerations for responsible use. In this course, students will apply concepts learned in AIN 1000.

Artificial Intelligence Foundations: Principles and Practice is a course designed as an introduction to artificial intelligence (AI), covering foundational concepts, data handling, and common misconceptions. Learners will explore how AI works through hands-on, no-code experiences in machine learning, generative AI, and automation tools. Real-world applications are highlighted across industries, with case studies and emerging trends shaping the future of AI. Emphasis is also placed on ethical AI practices, including fairness, privacy, and regulatory considerations for responsible use.

Introduction to Generative AI with Python and LLMs: Principles is the first part of a course which introduces the foundational concepts needed to begin working with generative AI tools, with an emphasis on how large language models (LLMs) process and generate content. Designed for learners from any professional background, students will gain hands-on experience applying simple coding logic and prompts to explore real-world uses of generative AI.

Introduction to Generative AI with Python and LLMs: Practice is the second part of a course which introduces the foundational concepts needed to begin working with generative AI tools, with an emphasis on how large language models (LLMs) process and generate content. Designed for learners from any professional background, students will gain hands-on experience applying simple coding logic and prompts to explore real-world uses of generative AI.

Introduction to Generative AI with Python and LLMs: Principles and Practice introduces the foundational concepts needed to begin working with generative AI tools, with an emphasis on how large language models (LLMs) process and generate content. Designed for learners from any professional background, students will gain hands-on experience applying simple coding logic and prompts to explore real-world uses of generative AI.

Application Development with LLMs: Principles is the first part of a course which enables learners to design and build real-world applications using LLMs and Python. Learners will explore advanced prompting techniques, integrate LLMs using APIs, develop multi-step workflows through prompt chaining, and use retrieval techniques using embeddings and vector search. Emphasizing the idea of vibe coding where LLMs assist with generating, debugging, and iterating on code, this course helps learners build useful tools such as writing assistants, and feedback bots. Furthermore, this course prepares learners for deeper architectural, retrieval-based, and fine-tuning workflows in Course 3, Building AI Agent Systems. 

Application Development with LLMs: Principles is the second part of a course which enables learners to design and build real-world applications using LLMs and Python. Learners will explore advanced prompting techniques, integrate LLMs using APIs, develop multi-step workflows through prompt chaining, and use retrieval techniques using embeddings and vector search. Emphasizing the idea of vibe coding where LLMs assist with generating, debugging, and iterating on code, this course helps learners build useful tools such as writing assistants, and feedback bots. Furthermore, this course prepares learners for deeper architectural, retrieval-based, and fine-tuning workflows in Course 3, Building AI Agent Systems. 

Application Development with LLMs: Principles and Practice enables learners to design and build real-world applications using LLMs and Python. Learners will explore advanced prompting techniques, integrate LLMs using APIs, develop multi-step workflows through prompt chaining, and use retrieval techniques using embeddings and vector search. Emphasizing the idea of vibe coding where LLMs assist with generating, debugging, and iterating on code, this course helps learners build useful tools such as writing assistants, and feedback bots. Furthermore, this course prepares learners for deeper architectural, retrieval-based, and fine-tuning workflows in Course 3. 

Building AI Agent Systems: Principles is the first part of a course which groups the foundational and applied skills from the previous two courses to explore advanced generative AI architectures and multimodal systems. Learners will deepen their understanding of retrieval-augmented generation (RAG), vector databases, and lightweight fine-tuning techniques such as LoRA. The course also introduces image generation on tools and multimodal prompting strategies to help students design creative and personalized applications. Ethical considerations and system safety are incorporated to prepare learners for responsible deployment. 

Building AI Agent Systems: Principles is the second part of a course which groups the foundational and applied skills from the previous two courses to explore advanced generative AI architectures and multimodal systems. Learners will deepen their understanding of retrieval-augmented generation (RAG), vector databases, and lightweight fine-tuning techniques such as LoRA. The course also introduces image generation on tools and multimodal prompting strategies to help students design creative and personalized applications. Ethical considerations and system safety are incorporated to prepare learners for responsible deployment. 

Building AI Agent Systems groups the foundational and applied skills from the previous two courses to explore advanced generative AI architectures and multimodal systems. Learners will deepen their understanding of retrieval-augmented genera on (RAG), vector databases, and lightweight fine-tuning techniques such as LoRA. The course also introduces image genera on tools and multimodal prompting strategies to help students design creative and personalized applications. Ethical considerations and system safety are incorporated to prepare learners for responsible deployment.