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Ashish Pratap Singh has curated a GitHub repository containing the best free courses, articles, tutorials, and videos to master AI Engineering. The repository covers:
– Mathematical Foundations
– AI & ML Fundamentals
– Deep Learning & Specializations
– Generative AI
– Large Language Models (LLMs)
– Prompt Engineering Guides
– RAG, Agents, and MCP
🔗 GitHub Repository: https://lnkd.in/gn-uSt9J
You Should Know:
1. Setting Up Your AI Learning Environment
To get started with AI engineering, you need a proper setup. Here are essential commands to prepare your system:
For Linux (Ubuntu/Debian):
sudo apt update && sudo apt upgrade -y sudo apt install python3 python3-pip git -y pip3 install numpy pandas matplotlib scikit-learn tensorflow torch jupyterlab
For Windows (PowerShell):
winget install Python.Python.3.10 pip install numpy pandas matplotlib scikit-learn tensorflow torch jupyterlab
2. Cloning the Repository
git clone https://github.com/ashishps1/learn-ai-engineering.git cd learn-ai-engineering
3. Running Jupyter Notebook for Interactive Learning
jupyter lab
4. Key AI/ML Commands
- Train a basic ML model (Scikit-learn):
from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier </li> </ul> iris = load_iris() X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2) model = RandomForestClassifier() model.fit(X_train, y_train) print("Accuracy:", model.score(X_test, y_test))- Fine-tuning an LLM (Hugging Face):
from transformers import pipeline </li> </ul> generator = pipeline("text-generation", model="gpt2") print(generator("AI will revolutionize", max_length=50))5. Essential Linux Commands for AI Workflows
nvidia-smi Check GPU usage (for deep learning) htop Monitor system resources tmux Persistent terminal sessions for long-running training
What Undercode Say
This repository is a goldmine for AI enthusiasts. Combining structured learning with hands-on coding accelerates mastery. Future AI engineers should:
– Contribute back via pull requests.
– Automate workflows using `cron` (Linux) or Task Scheduler (Windows).
– Experiment with cloud AI tools (AWS SageMaker, Google Colab).Prediction
AI education will become more open-source, with repositories like this replacing traditional courses. Expect AI-powered tutors by 2026.
Expected Output:
A fully functional AI learning environment with access to free, high-quality AI resources. 🚀
References:
Reported By: Ashishps1 Github – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅Join Our Cyber World:
- Fine-tuning an LLM (Hugging Face):
