Data Science Tutorials

Explore the different methods for filtering out and permanently removing duplicate rows using SQL. Learn the practical applications of how to remove duplicates in SQL Server, MySQL, and PostgreSQL.

Learn how to model and solve optimization problems using Pyomo, a powerful Python library. Explore practical examples from linear and nonlinear optimization!

Learn how to streamline your LLM evaluations with MLflow. This guide covers MLflow setup, logging metrics, tracking experiment versions, and comparing models to make informed decisions for optimized LLM performance!

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.