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. 157
    Parameter Tuning with Optuna

    Master Optuna for hyperparameter tuning in Python. This practical lesson covers core concepts, step-by-step implementation, troubleshooting, and next steps for your Applied AI engineering path.

  2. 158
    Track Experiments with Weights & Biases

    Learn to log, compare, and optimize ML experiments with Weights & Biases in this hands-on tutorial for Python developers.

  3. 159
    Automate Hyperparameter Sweeps

    Automate hyperparameter sweeps: learn to systematically search hyperparameters, run hands-on experiments, avoid common pitfalls, and know what to study next in this Applied AI engineering tutorial.

  4. 160
    Collaborative Filtering Systems

    Learn to build recommender systems with collaborative filtering in this Applied AI engineering lesson. Understand core concepts, implement hands-on steps, troubleshoot edge cases, and explore what to learn next.

  5. 161
    Matrix Factorization for Recommendations

    Implement matrix factorization for recommendations in Python — learn the core idea, step-by-step math, and a hands-on exercise. Covers options, edge cases, and next steps.

  6. 162
    Add Content-Based Filtering

    Add content-based filtering — Applied AI engineering.

  7. 163
    Hybrid Recommenders with Ensembles

    Learn hybrid recommenders with ensembles in this hands-on Applied AI engineering lesson. Understand the core concept, step-by-step implementation, options comparison, troubleshooting, and next steps. Perfect for developers following a structured learning path.

  8. 164
    Evaluate Recommenders with Precision@k

    Evaluate recommenders with precision@k — Applied AI engineering.

  9. 165
    Build a Rule-Based Chatbot

    Build a simple chatbot with rule logic in this Applied AI engineering tutorial. Step-by-step, hands-on, with troubleshooting and next steps.

  10. 166
    Intent Classification for Chatbots

    Learn how to use intent classification for chatbots in this hands-on Applied AI engineering lesson.

  11. 167
    Entity Extraction with spaCy

    Master entity extraction with spaCy in this hands-on Applied AI engineering tutorial. Learn core concepts, practical steps, troubleshooting, and next steps.

  12. 168
    Build a Q&A System with BERT

    Learn to build a question answering system with BERT in this hands-on Applied AI engineering tutorial. Step-by-step guidance, troubleshooting, and next steps to master extractive QA.

    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.