Fundamental Concepts for AI Developers
In this 5 session free course we will cover the concepts and fundamentals needed for becoming an AI Developer.
Knowledge of Python is preferred.
This curriculum should provide a solid foundation and help the students to explore further and become confident AI Developers.
Prerequisites: Preliminary Python skills, familiarity with APIs, data handling (JSON, basic data structures).
Tools & Environment:
- Python 3.8+
- VS Code with Python extension (Cascade)
- Jupyter Notebooks
- Standard libraries: requests, numpy, pandas, scikit-learn
- AI-specific libraries: openai, anthropic, google-generativeai, ollama, langchain, llama-index, sentence-transformers, faiss-cpu (or chromadb), transformers, datasets, peft, bitsandbytes (for finetuning)
- Access to cloud LLM APIs (OpenAI, Anthropic, Google Gemini - provide API keys or guide setup)
- Ollama installed locally.
Course content:
- What is AI? What is Machine Learning (ML)? What is Deep Learning (DL)?
- Types of ML: Supervised (Classification, Regression), Unsupervised (Clustering, Dimensionality Reduction), Reinforcement Learning (brief overview).
- Key Terminology: Features, Labels, Training Data, Test Data, Validation Data, Model, Algorithm, Inference, Overfitting, Underfitting.
- Basic Evaluation Metrics: Accuracy, Precision, Recall, F1-score (for classification), MSE/MAE (for regression).
- The ML Workflow: Data Collection -> Data Preprocessing -> Model Training -> Model Evaluation -> Deployment -> Monitoring.
- Predictive AI Recap: Focus on practical applications – what business problems can it solve? (e.g., fraud detection, customer churn, sales forecasting). Mention common model types at a high level (Decision Trees, Random Forests, Gradient Boosting, Neural Networks - no deep dive).
- Generative AI (GenAI): What is it? How does it differ from predictive AI?
- Types of GenAI: Text (LLMs), Images (GANs, Diffusion Models), Audio, Code.
- Common Use Cases: Content creation, chatbots, code generation, summarization, translation, synthetic data generation.
- Key Tools/Platforms: Hugging Face (as a hub for models & datasets), specific model providers (OpenAI, Anthropic, Google).
- What are Large Language Models (LLMs)? Transformer architecture (high-level overview: attention mechanism).
- Key Concepts: Prompts, Context Window, Tokens, Temperature, Top-p, Top-k.
- Pre-training vs. Fine-tuning (brief introduction, more in Module 5).
- Cloud-hosted LLMs:
- Providers: OpenAI (GPT series), Anthropic (Claude series), Google (Gemini).
- Access via APIs, benefits (scale, latest models), considerations (cost, data privacy).
- Locally-hosted LLMs with Ollama:
- What is Ollama? Benefits (privacy, offline, cost control, experimentation).
- Running models like Llama, Mistral, etc.
- Hardware considerations (RAM, VRAM).
- Limitations of LLMs: Knowledge cut-offs, hallucinations, lack of access to private/real-time data.
- What is RAG? How does it address LLM limitations?
- Key Components of a RAG Pipeline:
- Data Loading & Chunking: Strategies for breaking down documents.
- Embedding Models: Converting text to dense vector representations (e.g., Sentence-Transformers).
- Vector Stores/Databases: Storing and efficiently querying embeddings (e.g., FAISS, ChromaDB, Pinecone, Weaviate).
- Retriever: Finding relevant chunks based on user query.
- LLM for Synthesis: Combining retrieved context with the query to generate an answer.
- CAG (Context Augmented Generation) as a broader term.
- What is an AI Agent? Beyond simple input-output.
- Core Components of an Agent:
- LLM as the "brain" or reasoning engine.
- Tools/Functions: Allowing the LLM to interact with the external world (APIs, databases, code execution).
- Planning: Breaking down complex tasks into steps (e.g., ReAct - Reason + Act, Chain of Thought).
- Memory: Short-term (context window), Long-term (often using RAG or vector stores).
- Single-Agent vs. Multi-Agent Systems (MAS).
- Frameworks: LangChain Agents, LlamaIndex Agents, AutoGen, CrewAI (briefly introduce).
