LLM Engineering & Generative AI · Tutorial tracks

Applied AI engineering

LLM APIs, structured outputs, retrieval, evaluation harnesses — Python-native application focus.

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Lesson outline

  1. 181
    Clustering with k-means

    Learn clustering with k-means for groups in this Applied AI engineering tutorial — hands-on steps, troubleshooting, and what to study next.

  2. 182
    DBSCAN for Density Clusters

    Learn to cluster data by density with DBSCAN in Python. This lesson covers the core concept, hands-on implementation, and how to tune eps and min_samples.

  3. 183
    Visualize Embeddings with t-SNE

    Learn to visualize embeddings with t-SNE in Python. This hands-on tutorial covers key concepts, a step-by-step workflow, practical code examples, troubleshooting, and what to explore next in the Applied AI engineering track.

  4. 184
    Semantic Search Engine

    Build a semantic search engine — Applied AI engineering. Learn to implement semantic search in Python, covering embeddings, vector similarity, and practical steps.

  5. 185
    FAISS Vector Similarity

    Implement vector similarity with FAISS in this Applied AI engineering lesson. Learn core concepts, hands-on steps, troubleshooting, and what to study next.

  6. 186
    Create a Knowledge Graph from Text

    Learn to build a knowledge graph from text using Python and LLMs. This tutorial covers entity extraction, relation mapping, and graph construction, with hands-on steps, troubleshooting, and what to study next.

  7. 187
    Use Neo4j for Graph Queries

    Use Neo4j for graph queries — Applied AI engineering.

  8. 188
    Build a RAG system with ChromaDB

    Build a RAG system with ChromaDB — Applied AI engineering. Hands-on tutorial to set up ChromaDB for retrieval-augmented generation, with practical steps, troubleshooting, and what to learn next.

  9. 189
    Cache Predictions with Redis

    Cache predictions with Redis — Applied AI engineering. Learn to store and reuse model outputs for speed and cost savings.

  10. 190
    Load test AI endpoints with Locust

    Load test AI endpoints with Locust — Applied AI engineering tutorial covering hands-on steps, troubleshooting, and what to study next.

  11. 191
    Unit Test Model Functions

    Write unit tests for model functions in this Applied AI engineering tutorial — practical steps, troubleshooting, and what to study next.

  12. 192
    Pytest for AI Code Coverage

    Use pytest for AI code coverage — Applied AI engineering.

    Applied AI engineering — step-by-step tutorials

    What you will find here

    This track walks through applied ai engineering in order — each lesson is server-rendered HTML you can read without JavaScript. Follow the outline, then practice in the browser IDE when a lesson links to runnable code.

    Tutorials vs quizzes and code samples

    Tutorials teach in sequence. For quick checks use quizzes. For copy-paste snippets see code samples. For deeper reading browse articles.