How to: Apr 19, 2024
1. Learn statistics for Data Science and AI Machine Learning. freeCodeCamp just published this handbook that will help you learn key concepts like Bayes' Theorem, Confidence Intervals, and the Central Limit Theorem. It covers both the classical math notation and Python implementations of these concepts. This is a broad primer for developers who are getting into stats, and it's also a helpful reference you can bookmark and pull up as needed. (full-length handbook): https://www.freecodecamp.org/news/statistics-for-data-scientce-machine-learning-and-ai-handbook/

2. And if you want to dig even further into applied Data Science, freeCodeCamp also published this in-depth Python course on A/B Testing and optimization. It will teach you core concepts like Hypothesis Testing, Statistical Significance Levels, Pooled Estimates, and P-values. (3 hour YouTube course): https://www.freecodecamp.org/news/applied-data-science-a-b-testing/

3. One of the most exciting areas of AI at the moment is Retrieval Augmented Generation (RAG). This freeCodeCamp Python course will teach you how to combine your own custom data with the power of Large Language Models (LLMs). You'll learn straight from a software engineer who works on the popular LangChain open source project, Dr. Lance Martin. (3 hour YouTube course): https://www.freecodecamp.org/news/mastering-rag-from-scratch/

4. This week I interviewed software engineer and visual artist Kass Moreno about her photo-realistic CSS art. Frankly you have to see her art to believe it. She painstakingly recreates manufactured objects like cameras, gameboys, and synthesizers using nothing but CSS. We talk about her childhood in Mexico and Texas, dropping out of architecture school, her listless years of working in retail, and how she ultimately learned to code using freeCodeCamp and got her first developer role. (1 hour watch or listen in your favorite podcast app): https://www.freecodecamp.org/news/css-artist-kass-moreno-freecodecamp-podcast-119/

5. Learn how to build your own movie recommendation engine using Python. You'll use powerful data libraries like scikit-learn, Pandas, and the Natural Language Toolkit. By the end of this tutorial, you'll have a tool that can recommend movies to you based on content and genre. (1 hour read): https://www.freecodecamp.org/news/build-a-movie-recommendation-system-with-python/

Quote of the Week: *"Python is everywhere at Industrial Light & Magic. It's used to extend the capabilities of our applications, as well as providing the glue between them. Every computer-generated image we create has involved Python somewhere in the process."* — Philip Peterson, Principal Engineer at ILM, the special effects company behind Star Wars and so many other Hollywood movies