Python Tutorial

Enhance your visualizations with Matplotlib colormaps. Learn to pick the right colormap, adjust color classes, and troubleshoot common visualization issues.

Learn about Snowflake Cortex AI and how it can be used for LLMs and machine learning.

Explore how linear search works and why it’s ideal for small, unsorted datasets. Discover simple Python implementations, including iterative and recursive methods, and learn when to choose linear search over other algorithms.

Create standout bar charts using Matplotlib, Seaborn, Plotly, Plotnine, and Pandas. Explore bar chart types, from simple vertical and horizontal bars to more complex grouped and stacked layouts.

Explore everything you need to know about how to implement the bag of words model in Python.

Discover the essentials of depth-first search for navigating graphs and trees. Implement DFS in Python using recursion and iteration, and see how DFS compares to breadth-first search and Dijkstra’s algorithm.

Learn how to use a Python API to connect systems and give your projects real-time data. Discover how to retrieve, send, and process data, and make your applications dynamic and responsive.

Discover how breadth-first search systematically explores nodes and edges in graphs. Learn its level-by-level approach to ensure the shortest path in unweighted networks. Apply BFS across data science, AI, and networking fields.

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

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.