How to Become Data Scientist or Machine Learnign Engineer with Free Resources

 


I have curated the learning pathway for you to learn Machine Learning and Data Science.

You can follow the Google Classroom paths below:

Also, you can find my Github repo for many examples for beginner to expert levels here.

Photo by Vanessa Loring

Popular posts from this blog

COBOT with GenAI and Federated Learning

Image
                                                                                 generated by meta ai The integration of Generative AI (GenAI) and Large Language Models (LLMs) is poised to significantly enhance the intelligence and capabilities of cobots, taking them beyond basic automation. Here's how: 1. Enhanced Natural Language Interaction: Intuitive Communication: LLMs enable cobots to understand and respond to natural language commands, making human-robot interaction far more intuitive. Instead of complex programming, workers can simply use voice commands or text instructions. This simplifies task assignment and allows for real-time adjustments. Contextual Understanding: LLMs provide cobots with a deeper understanding of context, allowi...
Read more

Self-contained Raspberry Pi surveillance System Without Continue Internet

Image
                                                                gemini generated A  self-contained Raspberry Pi surveillance system that: Runs autonomously (like EyeOS) — camera always active and streaming. Keeps searching for a known Wi-Fi hotspot (your phone) . Starts streaming automatically when the phone hotspot is available. Lets you view the camera feed on your phone (via browser or app). Here’s a detailed, production-ready setup: ⚙️ Step 1: Setup Raspberry Pi Camera & Software 1. Enable the camera sudo raspi-config Go to Interface Options → Camera → Enable Reboot: sudo reboot 2. Install dependencies sudo apt update sudo apt install python3-picamera2 python3-flask git -y 📸 Step 2: Create a Local Flask Streaming App Create file /home/pi/camera_stream.py : from flask import Flas...
Read more

AI in Education: Embracing Change for Future-Ready Learning

Image
  Executive Summary An adjunct professor advocates embracing AI in classrooms over prohibition, using historical examples of replaced skills (cursive, memorization) to argue for focusing on adaptability, creativity, and critical thinking. AI enables personalized tutoring, equity, and efficiency but demands literacy and ethics. Recommendations include shifting to debates over essays and principles over rote tasks to equip students for AI workplaces. Historical Context: Skills Evolved by Technology Education prioritizes future utility amid time constraints. Past emphases have faded: Cursive/penmanship : Graded courses in prior generations; now obsolete with keyboards—digital fluency matters more. Memorization (e.g., full periodic table) : Drills wasted time; databases provide facts—focus on conceptual understanding (e.g., element groups' properties). Manual arithmetic : Long division drills unnecessary post-fundamentals; calculators standard in higher education. Map reading : Seconda...
Read more