🧪 ML & AI Experiments with Colab

This repository showcases a hands-on journey through Machine Learning and AI using Google Colab and TensorFlow. Each project in this repo is part of a structured roadmap that builds foundational to advanced ML skills — including model training, evaluation, data handling, and visualization.

🚀 What's Inside?

• ✅ Image Classification with CNN (Cats vs Dogs)
• ✅ MNIST Digit Classifier
• 🔜 CIFAR-10 Deep Dive
• 🔜 Data Augmentation Techniques
• 🔜 Transfer Learning with Pre-trained Models
• 🔜 Custom Dataset Training & Evaluation
• 🔜 Model Deployment (Streamlit/FastAPI)

🛠️ Tech Stack

• Python 3
• TensorFlow / Keras
• Google Colab
• NumPy, Matplotlib, Sklearn

📁 Folder Structure

Each folder contains:

• notebook.ipynb: Colab notebook with code and outputs
• README.md: Project-specific summary
• assets/: Supporting images/files

📌 How to Use

1. Open any .ipynb notebook in Google Colab
2. Follow along with code, explanations, and visuals
3. Modify the code to try your own experiments
4. Use the models for predictions or fine-tune further

🎯 Purpose

The goal of this repo is to:

• Learn ML through projects — not theory alone
• Build a solid ML portfolio for job/internship showcasing
• Understand each model, tweak it, and improve it step-by-step

📚 License

This repository is free to use for learning and demonstration purposes.

Made with ❤️ by Sheesh Mohsin — follow the roadmap, experiment boldly, and grow consistently!