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…