Data Science Tutorials

Explore the arithmetic mean's role in data analysis. Learn its formula, applications, and how it compares to other kinds of means and other statistical measures, and understand when each is most useful.

Uncover the impact of multicollinearity on regression models. Discover techniques to detect multicollinearity and maintain model reliability. Learn how to address multicollinearity with practical solutions.

Learn about the RMSprop optimization algorithm, its intuition, and how to implement it in Python. Discover how this adaptive learning rate method improves on traditional gradient descent for machine learning tasks.

Machine learning is complex and often hard to wrap your head around. By visualizing machine learning models, you can get a great level of understanding of model performance and the decisions the model makes when making predictions.

Discover how the AdamW optimizer improves model performance by decoupling weight decay from gradient updates. This tutorial explains the key differences between Adam and AdamW, their use cases and provides a step-by-step guide to implementing AdamW in PyTorch.

Learn how to create and customize dumbbell charts in Tableau. Discover how to prepare your data, build the chart, and enhance it for impactful visual comparisons!

Learn everything you need to know about synthetic data generation. Discover the techniques and tools that make synthetic data essential for AI and machine learning with practical Python code examples to help you get started!

Learn to build AI chatbots with Streamlit, LangChain, and Neo4j. This tutorial covers creating UIs for LLM apps, implementing RAG, and deploying to Streamlit Cloud.

Discover the key differences, applications, and implementation of normalization and standardization in data preprocessing for machine learning.

Learn how to build real-time AI applications with OpenAI's Realtime API. This tutorial covers WebSockets, Node.js setup, text/audio messaging, function calling, and deploying a React voice assistant demo.

The SUMMARIZE() function in DAX creates summary tables by grouping data and applying aggregate functions in tools like Power BI and Excel Power Pivot. Keep reading to learn how to use DAX SUMMARIZE() to group and aggregate your data and derive good insights.

Discover Microsoft's new LLM series and boost your model's accuracy from 65% to 86% by fine-tuning it on the E-commerce classification dataset.