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AI University — All Lessons

Complete collection of all 36 lessons across 8 courses. Learn at your own pace.

AI Data

5 lessons
Datasets & Data Sources — This lesson covers the importance of datasets and data sources in building and training AI models, particularly large la… Data Preprocessing — This lesson covers the importance of data preprocessing in the context of modern AI, specifically large language models … Feature Engineering — This lesson covers the concept of feature engineering in modern AI, specifically how to extract and use features from da… Embeddings & Vector Representations — This lesson covers how words and data are converted into vector representations, allowing AI systems to understand and p… Data Pipelines & Augmentation — This lesson covers data pipelines and augmentation, essential concepts in AI that help improve model performance and red…

AI Training

5 lessons
Supervised Learning — This lesson covers supervised learning, a type of machine learning where a model is trained on labeled data to make pred… Unsupervised Learning — This lesson covers the basics of unsupervised learning in AI, specifically text clustering and topic modeling. It explai… Fine-Tuning Pretrained Models — Fine-tuning pretrained models is a way to unlock the capabilities of a model that are hard to access via prompting alone… Reinforcement Learning — This lesson covers reinforcement learning, a technique used to train large language models to generate responses that ar… Training Best Practices — This lesson covers the best practices for training large language models, including the progression path, hyperparameter…

Prompts & Prompt Engineering

5 lessons
Prompt Basics & Structure — This lesson covers the basics and structure of prompts, which are instructions given to AI models to generate specific o… Few-Shot & Zero-Shot Prompting — This lesson covers few-shot and zero-shot prompting, techniques used to guide large language models (LLMs) to perform sp… Chain-of-Thought Prompting — This lesson covers Chain-of-Thought (CoT) prompting, a technique to improve the performance of AI models by asking them … System Prompts & Personas — This lesson covers system prompts and personas in AI, including how to design and use them to improve model performance … Advanced Prompt Techniques — This lesson covers advanced prompt techniques for working with artificial intelligence (AI) and large language models (L…

Inference

4 lessons
How Inference Works — This lesson covers how inference works in AI systems, including the challenges and techniques used to optimize model per… Model Serving Architectures — This lesson covers the basics of Model Serving Architectures, including the simplest architecture and progressive additi… Quantization & Optimization — This lesson covers the basics of quantization and optimization in AI systems, specifically how to reduce model size and … Batching, Caching & Latency — This lesson covers strategies to improve the performance of large language models (LLMs) in real-world applications. We'…

AI Hosting & Deployment

4 lessons
Deployment Strategies — This lesson covers the strategies for deploying large language models (LLMs) and other AI applications, including model … Containers, Scaling & Orchestration — This lesson covers containers, scaling, and orchestration in the context of modern AI/LLM/GenAI systems. It explains how… Serverless & Edge Deployment — This lesson covers the basics of serverless and edge deployment for AI applications, including the benefits and challeng… Monitoring & Cost Optimization — This lesson covers the importance of monitoring and cost optimization in AI applications, especially when using large la…

RAG — Retrieval-Augmented Generation

4 lessons
What is RAG? — In this lesson, we'll cover Retrieval-Augmented Generation (RAG), a technique used in modern AI systems to improve their… Chunking & Embedding Strategies — This lesson covers the "Chunking & Embedding Strategies" used in modern AI systems, specifically in Retrieval-Augmented … Vector Search & Retrieval — This lesson covers vector search and retrieval, a crucial technique in AI that enables finding similar vectors efficient… Hybrid & Advanced Retrieval — This lesson covers the concept of hybrid and advanced retrieval in AI, specifically how to improve the performance of la…

AI Agents

5 lessons
What Are AI Agents? — This lesson introduces the concept of AI agents and learning methods. We will explore different types of agents, their c… Tool Use & Function Calling — This lesson covers the concept of tool use and function calling in AI, which enables agents to perform complex tasks by … Planning & Reasoning in Agents — This lesson covers planning and reasoning in artificial intelligence (AI) agents, including goal-based agents, plan moni… Memory & State in Agents — This lesson covers how agents in AI remember and make decisions based on their state. It introduces different types of a… Multi-Agent Systems — This lesson covers the basics of multi-agent systems, including environments, agents, and interactions. We'll explore ho…

Deep Learning Basics

4 lessons
Neural Networks Fundamentals — This lesson covers the basics of neural networks, including feedforward networks, deep learning, and gradients. We'll al… Backpropagation & Gradient Descent — This lesson covers the basics of backpropagation and gradient descent, two key techniques used in modern AI systems to o… Convolutional Neural Networks — This lesson covers the basics of Convolutional Neural Networks (CNNs), a type of neural network that's particularly effe… Transformers & Attention — This lesson covers the Transformer architecture and its attention mechanism, which allows models to focus on relevant pa…