I will develop text and image classification models with python
Machine Learning Engineer for LLM Agents RAG and Python Automation
About this Gig
Turn your labeled text or images into a practical Python classification model. I am an AI/ML engineer with 4+ years of experience in Python, NLP and API development.
I can help with sentiment analysis, spam detection, topic labeling or image category recognition. Each package covers ONE agreed text OR image classification task.
Basic: baseline model, up to 2 classes / 1,000 labeled samples.
Standard: tuned model, up to 5 classes / 5,000 labeled samples, with evaluation and error analysis.
Premium: up to 10 classes / 10,000 labeled samples, plus a FastAPI prediction endpoint and Docker setup.
All packages include basic preprocessing, Python source code, saved model, setup instructions and held-out evaluation metrics. Model choice depends on your data and agreed compute budget. Accuracy is measured, not guaranteed.
You provide a labeled dataset you have permission to use. Data labeling, paid compute, hosting, cloud deployment, authentication and live integrations require separate scope. Revisions cover the agreed task.
Please message me with sample data, class labels and your goal before ordering so we can confirm feasibility.
Expertise:
Image processing
•
Classification
•
Sentimental analysis
Programming language:
Python
My Portfolio
FAQ
Does one package include both text and image classification?
No. Each package covers one text OR image classification task on one agreed dataset. If you need both, message me for a custom offer with separate scope and deliverables.
What dataset do I need to provide?
Provide labeled text in CSV/JSON or labeled images in folders with a label file. Include class definitions and sample counts. You must have permission to use the data. Data collection and labeling are outside the packages; share a sample before ordering.
Can you guarantee a specific accuracy?
No. Performance depends on dataset size, label quality, class balance and task difficulty. I report held-out metrics such as precision, recall, F1 and a confusion matrix, and agree on realistic evaluation goals after reviewing your sample data.
