# 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 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.