Learn AI and LLMs from Scratch Using Free Resources

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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:

      💬 Whatsapp | 💬 Telegram

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