Machine Learning Project: Social Media Marketing in Python

About this course

What if you could predict how well a Reddit comment will perform before you post it?

In this short, project-based course, you’ll build real machine learning and AI models that predict the score of a Reddit comment; turning social media engagement into a concrete, data-driven problem you can solve with Python.

This is not a theory-heavy course. You’ll work hands-on with modern NLP tools, transformer models, and large language models, and you’ll compare multiple approaches to see what actually works best in practice.

What You'll Build

By the end of the course, you’ll have a complete ML pipeline that can:

• Predict whether a Reddit comment will receive a positive or negative score
• Predict the actual score value of a comment
• Compare traditional ML, fine-tuned transformers, and state-of-the-art LLMs

What You’ll LearnTransformer Fine-Tuning (Hugging Face) - Fine-tune a transformer model for classification (Will this comment get upvoted or downvoted?) and regression (What score is this comment likely to receive?)Generative AI & LLM Evaluation - Use a state-of-the-art large language model for zero-shot classification. Predict Reddit comment performance without collecting or training on a dataset. Compare an LLM’s performance against models you train yourself.

Classical Machine Learning Baselines - Build strong baselines using traditional NLP and machine learning techniques. See how modern transformers actually compare to older, simpler approaches.

Why This Course Is Different

Short and focused – no filler, no unnecessary theory

End-to-end project – from data to predictions

Modern tools – Hugging Face, transformers, and LLMsReal-world relevance – social media marketing meets applied ML

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