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The Microsoft AI Skills Fest is a groundbreaking 50-day global event dedicated to advancing skills in Artificial Intelligence (AI). Featuring live sessions with Microsoft and industry experts, hands-on hackathons, self-guided training, and community-driven events, this initiative aims to empower participants with cutting-edge AI knowledge. A highlight includes an attempt to break the GUINNESS WORLD RECORDS™ for the largest online AI lesson in 24 hours!
🔗 Official Event URL: https://lnkd.in/dRK-RNih
You Should Know: Practical AI/ML Commands & Tools
1. Setting Up an AI Development Environment
- Linux/Mac:
Install Python and pip sudo apt-get install python3 python3-pip Set up a virtual environment python3 -m venv ai_env source ai_env/bin/activate Install Jupyter Notebook for AI prototyping pip install jupyter numpy pandas scikit-learn tensorflow
Windows (PowerShell):
Install Chocolatey (package manager) Set-ExecutionPolicy Bypass -Scope Process -Force iex ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1')) Install Python and Jupyter choco install python jupyter
2. Running Your First ML Model
Sample Python code for a linear regression model
from sklearn.linear_model import LinearRegression
import numpy as np
Sample data
X = np.array([[bash], [bash], [bash]])
y = np.array([2, 4, 6])
Train the model
model = LinearRegression().fit(X, y)
print(f"Prediction for X=4: {model.predict([[bash]])}")
3. Azure AI CLI Integration
Install Azure CLI curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash Login and deploy an AI service az login az ml workspace create -n MyAIWorkspace -g MyResourceGroup
4. Docker for AI Reproducibility
Pull a pre-built ML Docker image docker pull tensorflow/tensorflow:latest Run a Jupyter server in Docker docker run -p 8888:8888 tensorflow/tensorflow:latest-jupyter
What Undercode Say
The AI Skills Fest is a golden opportunity to dive into AI with structured guidance. Complement event learnings with hands-on practice:
– Use Linux commands (grep, awk) to clean datasets.
– Automate workflows with Cron jobs (e.g., 0 python /path/to/ai_script.py).
– Explore Windows WSL for seamless Linux/Windows AI development.
– Leverage Git (`git clone https://github.com/microsoft/ai.git`) for collaborative projects.
Expected Output:
Prediction for X=4: [8.]
🔗 Additional Resources:
References:
Reported By: Edesan Tomaz – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
