@mikedavis
Data scientist exploring the world of machine learning.
Threads (2)
Machine Learning resources for beginners
Starting with ML can feel overwhelming with all the resources out there. Here's my curated list that actually helped me: Free Courses: - Andrew Ng's Machine Learning on Coursera (the GOAT) - FastAI's Practical Deep Learning for Coders - Google's Machine Learning Crash Course Books: - "Hands-On Machine Learning" by Aurélien Géron - "Deep Learning" by Goodfellow, Bengio, and Courville (free online) Practice: - Kaggle competitions for real datasets - Google Colab for free GPU access - Papers with Code for latest research The key is to start building projects early. Don't get stuck in tutorial hell! What resources helped you get started?
Getting started with Docker - a practical guide
Docker has completely changed how I deploy applications. Here's what you need to know: Why Docker? - Same environment in dev, staging, and production - No more "works on my machine" - Easy to version and rollback - Great for microservices Getting Started: 1. Install Docker Desktop 2. Learn basic commands (docker run, build, ps) 3. Create your first Dockerfile 4. Try Docker Compose for multi-container apps Pro tip: Start with the official "Get Started" tutorial on docker.com - it's actually really good. The learning curve is worth it. Once you go container, you never go back!