> For the complete documentation index, see [llms.txt](https://data-distilller.gitbook.io/adobe-data-distiller-guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning.md).

# Unit 8: DATA DISTILLER STATISTICS & MACHINE LEARNING 

- [STATSML 100: Python & JupyterLab Setup for Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-100-python-and-jupyterlab-setup-for-data-distiller.md): Learn how to setup Python and JupyterLab to connect to Data Distiller.
- [STATSML 101: Learn Basic Python Online](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-101-learn-basic-python-online.md): The goal of this module is to teach you basic Python so that you can understand any code that you come across.
- [STATSML 200: Unlock Dataset Metadata Insights via Adobe Experience Platform APIs and Python](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-200-unlock-dataset-metadata-insights-via-adobe-experience-platform-apis-and-python.md): This chapter covers the essential steps for installing necessary libraries, generating access tokens, and making authenticated API requests.
- [STATSML 201: Securing Data Distiller Access with Robust IP Whitelisting](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-201-securing-data-distiller-access-with-robust-ip-whitelisting.md): Secure Access, Simplified: Protect Data Distiller with IP Whitelisting
- [\[DRAFT\]STATSML 201: Unlocking Dataset Insights with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/draft-statsml-201-unlocking-dataset-insights-with-data-distiller.md)
- [STATSML 300: AI & Machine Learning: Basic Concepts for Data Distiller Users](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-300-ai-and-machine-learning-basic-concepts-for-data-distiller-users.md): Unlock the power of AI and machine learning in this course—equipping you with the basic concepts  to make a real-world impact
- [STATSML 301: A Concept Course on Language Models](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-301-a-concept-course-on-language-models.md): Learn the key ideas behind language models
- [STATSML 302: A Concept Course on Feature Engineering Techniques for Machine Learning](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-302-a-concept-course-on-feature-engineering-techniques-for-machine-learning.md): Transform raw data into predictive power with essential feature engineering techniques.
- [STATSML 400: Data Distiller Basic Statistics Functions](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-400-data-distiller-basic-statistics-functions.md): Unlock the Power of Data: Master Every Key Statistical Function in Data Distiller
- [STATSML 500: Generative SQL with Microsoft GitHub Copilot, Visual Studio Code and Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-500-generative-sql-with-microsoft-github-copilot-visual-studio-code-and-data-distiller.md): Streamline your development workflow with Visual Studio Code and Github Copilot—fast, lightweight, and customizable for all your coding needs, from generating SQL queries to managing projects.
- [STATSML 600: Data Distiller Advanced Statistics & Machine Learning Models](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-600-data-distiller-advanced-statistics-and-machine-learning-models.md): Discover advanced statistics and machine learning functions to build predictive models
- [STATSML 601: Building a Period-to-Period Customer Retention Model Using Logistics Regression](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-601-building-a-period-to-period-customer-retention-model-using-logistics-regression.md): Unlocking Future Engagement: Data-Driven Retention Predictions for Smarter Personalization Strategies
- [STATSML 602: Techniques for Bot Detection in Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-602-techniques-for-bot-detection-in-data-distiller.md): Turn clicks into insights: Discover how SQL can reveal bot behavior
- [STATSML 603: Predicting Customer Conversion Scores Using Random Forest in Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-603-predicting-customer-conversion-scores-using-random-forest-in-data-distiller.md): Transform Data Into Action: Predict, Personalize, Prosper!
- [STATSML 604: Data Exploration for Customer AI in Real-Time Customer Data Platform](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-604-data-exploration-for-customer-ai-in-real-time-customer-data-platform.md)
- [STATSML 604: Predicting Customer Journey Insights for Airlines](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-604-predicting-customer-journey-insights-for-airlines.md)
- [STATSML 604: Car Loan Propensity Prediction using Logistic Regression](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-604-car-loan-propensity-prediction-using-logistic-regression.md)
- [STATSML 700: Sentiment-Aware Product Review Search with Retrieval Augmented Generation (RAG)](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-700-sentiment-aware-product-review-search-with-retrieval-augmented-generation-rag.md): This tutorial demonstrates how to implement a Retrieval-Augmented Generation (RAG) architecture using Python, LangChain and Hugging Face Transformers.
- [STATSML 800: Turbocharging Insights with Data Distiller: A Hypercube Approach to Big Data Analytics](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-800-turbocharging-insights-with-data-distiller-a-hypercube-approach-to-big-data-analytics.md): Turning Big Data into Big Insights with Speed, Precision, and Scalability