**My First Machine Learning Project: Sentiment Analysis with Hugging Face**
As a recent graduate from the University of Maryland Global Campus with a BS in Software Development & Security, I wanted to dive into practical machine learning applications. I decided to create a sentiment analysis model using Hugging Face's transformers library - a perfect blend of my interests in AI and software development.

**What I Built**
I developed a sentiment analysis model that can determine whether a piece of text expresses positive or negative sentiment. The model is built on top of DistilBERT (a lighter, faster version of BERT) and trained on the IMDB movie reviews dataset. What's cool is that anyone can now use my model through the Hugging Face Hub!

**Technical Details**
Base Model: I used distilbert-base-uncased as my starting point
Dataset: Trained on IMDB reviews (1000 training samples, 200 test samples)
Metrics: The model tracks accuracy, F1 score, precision, and recall
Framework: Built with Hugging Face Transformers and PyTorch
Deployment: Model is publicly available on Hugging Face Hub

**Key Learnings**
Practical Experience with Transformers: Got hands-on experience with state-of-the-art NLP models
Real-world ML Deployment: Learned how to train and deploy a model to production
Performance Optimization: Worked with batch sizes and learning rates to optimize training
Version Control: Used Git and Hugging Face's model versioning system
Future Improvements

**I plan to:**
Expand the training dataset size
Experiment with different model architectures
Add support for more nuanced sentiment categories
Improve the model's performance on edge cases
You can check out the model and try it yourself at: https://huggingface.co/Freakid/sentiment-analysis-model

This project was a great way to apply my academic knowledge to a real-world problem, and I'm excited to build more AI/ML projects in the future!