Machine Learning Project: Social Media Marketing in Python

Reddit Comment Score Prediction and Data Analytics with AI Language Models

What you’ll learn

• Fine-tune a transformer language model to predict Reddit comment scores
• Understand the correct approach to a machine learning project, including establishing a baseline

Requirements

• Experience with classification and regression using neural networks
• Knowledge of loss functions for classification and regression
• Python programming experience
• Knowledge of metrics like accuracy, MSE, F1-score
• Understand train-test splitting, overfitting, generalization
• Knowledge of text-preprocessing: tokenization, truncation
• Understand the importance of context window length / maximum sequence length for sequence models
• Understand the concept of fine-tuning (the code and syntax will be shown to you)

Description

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

What You’ll Learn

• Transformer 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?)
• 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 LLMs
• Real-world relevance – social media marketing meets applied ML

You’ll walk away with practical experience that applies not just to Reddit, but to any engagement prediction, text scoring, or NLP analytics problem in marketing, content creation, or social platforms.

Who This Course Is For

• Beginners to intermediate Python users interested in ML and AI
• Marketers and creators curious about data-driven social media optimization
• Developers and data scientists who want a hands-on NLP project
• Anyone who wants to learn how transformers and LLMs perform in the real world

If you’ve ever wondered whether modern AI can actually help you write better-performing content-and how it compares to models you train yourself-this course is for you.

Who This Course Is Not For

This course is not for everyone! Because this isn’t a theory-based course, you’ll have to be willing to catch up on certain topics on your own if you don’t already have experience with them (e.g. fine-tuning, loss functions, text preprocessing, etc.).

Suggested Prerequisites

• Experience with classification and regression using neural networks
• Knowledge of loss functions for classification and regression
• Python programming experience
• Knowledge of metrics like accuracy, MSE, F1-score
• Understand train-test splitting, overfitting, generalization
• Knowledge of text-preprocessing: tokenization, truncation
• Understand the importance of context window length / maximum sequence length for sequence models
• Understand the concept of fine-tuning (the code and syntax will be shown to you)

Who this course is for:

• Beginners to intermediate Python users interested in ML and AI
• Marketers and creators curious about data-driven social media optimization
• Developers and data scientists who want a hands-on NLP project
• Anyone who wants to learn how transformers and LLMs perform in the real world

Homepage