Is Machine Learning still worth learning in 2026?
8 reasons why there's still great value in learning how to do supervised Machine Learning yourself (rather than relying on an LLM)
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What is "AI" versus "Machine Learning"? 🤔
AI is a field that creates intelligent systems, and Machine Learning is the dominant approach for achieving that intelligence by learning patterns from data.
Book preview: Master Machine Learning with scikit-learn 📖
This is a practical guide to help you transform from Machine Learning novice to skilled ML practitioner. Download the first 3 chapters for free!
AI progress in 2025 📈
Thoughts on the current state of AI progress and the most important developments in 2025
How to calculate "scoring streaks" with pandas 🏀
Learn how to identify & analyze consecutive events in your data using advanced DataFrame methods!
Should you still learn to code in 2025? 🤔
Is coding dead in the age of AI? Not at all! Here are 16 reasons why coding is a must-have skill for 2025 and beyond.
How to keep up with AI in 2025 🏃♂️
These 7 AI experts will guide you through the most important developments in Artificial Intelligence.
Stop overthinking your AI prompts 🧠
Writing effective prompts for ChatGPT doesn't have to be complicated! Learn six simple tips for getting better results from any AI model.
Use premium AI models for pennies 💰
Learn how to access ChatGPT, Claude, and more for pennies per conversation rather than paying for expensive subscriptions!
Upgrade your terminal app 🚀
Tired of wrestling with your terminal? Try Warp, a modern & intelligent app that is packed with power user features and easily customizable.
How to prevent data leakage in pandas & scikit-learn ☔
What is data leakage, why is it problematic, and how can you prevent it when working on a supervised Machine Learning problem in Python?
Should you discretize continuous features for Machine Learning? 🤖
Learn how to "discretize" or "bin" your continuous features using Python's scikit-learn, and find out why I usually don't recommend doing so.
Jupyter & IPython terminology explained 💡
Are you trying to understand the differences between Jupyter Notebook, JupyterLab, IPython, Colab, and related terms? You're in the right place!
Get started with conda environments 🤝
Discover the benefits of virtual environments and learn the six conda commands you need to know to get started!
What are conda, Anaconda, and Miniconda? 🐍
What is the Anaconda distribution and why do people use it? How is it related to conda & Miniconda? As a Data Scientist, which should I use?