Kevin Markham    8 reasons why there’s still great value in learning how to do supervised Machine Learning yourself (rather than relying on an LLM)

Kevin Markham    AI is a field that creates intelligent systems, and Machine Learning is the dominant approach for achieving that intelligence by learning patterns...

Kevin Markham    This is a practical guide to help you transform from Machine Learning novice to skilled ML practitioner. Download the first 3 chapters for free

Kevin Markham    Thoughts on the current state of AI progress and the most important developments in 2025

Kevin Markham    Learn how to identify & analyze consecutive events in your data using advanced DataFrame methods

Kevin Markham    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.

Kevin Markham    These 7 AI experts will guide you through the most important developments in Artificial Intelligence.

Kevin Markham    Writing effective prompts for ChatGPT doesn’t have to be complicated Learn six simple tips for getting better results from any AI model.

Kevin Markham    Learn how to access ChatGPT, Claude, and more for pennies per conversation rather than paying for expensive subscriptions

Kevin Markham    Tired of wrestling with your terminal? Try Warp, a modern & intelligent app that is packed with power user features and easily customizable.

Kevin Markham    What is data leakage, why is it problematic, and how can you prevent it when working on a supervised Machine Learning problem in Python?

Kevin Markham    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.

Kevin Markham    Are you trying to understand the differences between Jupyter Notebook, JupyterLab, IPython, Colab, and related terms? You’re in the right place

Kevin Markham    Discover the benefits of virtual environments and learn the six conda commands you need to know to get started

Kevin Markham    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?