Kaggle LLM Classification Finetuning

About this course

This course takes you through Kaggle's collection of practical notebooks on fine-tuning large language models (LLMs) for classification tasks. You'll work with real-world examples and code, learning how to adapt pre-trained models to solve text classification problems—a skill that powers everything from sentiment analysis to intent detection in chatbots. Kaggle's community-driven approach means you're learning from practitioners who've already solved these problems in production.

What you'll learn

• How to load and prepare text data for LLM fine-tuning, including handling imbalanced datasets
• Core fine-tuning techniques: setting learning rates, choosing optimizers, and avoiding overfitting on smaller datasets
• How to adapt pre-trained models like BERT, RoBERTa, or GPT variants for multi-class and binary classification
• Evaluation metrics specific to classification (precision, recall, F1-score) and when to use each
• Practical strategies for reducing computational cost and training time on limited hardware
• How to structure Kaggle notebooks for reproducibility and iterate on experiments quickly
• Common pitfalls in fine-tuning and how to debug when model performance plateaus

Who this is for

You're an intermediate learner ready to move beyond tutorials into hands-on experimentation. You've already worked with basic Python and understand what a machine learning model is—now you want to fine-tune LLMs for real problems.

• Data Scientists — master fine-tuning workflows to accelerate your project cycles and ship classification models faster
• Machine Learning Engineers — build production-grade text classification pipelines using industry-standard techniques

Prerequisites

You should be comfortable with Python, understand basic machine learning concepts (training/validation/test splits, loss functions), and have some exposure to how neural networks work. Familiarity with transformers and hugging face libraries is helpful but not required—the course will reinforce these as you go.

Why this matters for Indian learners

Text classification and LLM fine-tuning skills are in high demand across Indian tech hubs, from Bangalore startups building AI products to established companies like Flipkart, Ola, and Bajaj Auto deploying NLP systems at scale. Learning to fine-tune LLMs on modest hardware is especially valuable in India, where cloud compute costs matter more than in Western markets. These skills directly translate to roles in AI research teams, product companies, and consulting firms that command competitive salaries in India's AI-first economy.

Frequently asked questions

Is this course really free?

Yes, completely free. Kaggle notebooks are open and free to run, and you can learn without any paid tier or subscription.

How long will it take to complete?

The course is designed as a 10-hour commitment. Spread over 2–3 weeks, that's roughly 3–5 hours per week, giving you time to experiment and tweak code between sessions. If you have more time, you can accelerate; if you want to linger on harder concepts, take it slower.

Will I get a certificate?

This course doesn't issue a formal certificate. However, completing notebooks on Kaggle and sharing them publicly builds a portfolio piece that's often more valuable to employers than a certificate—hiring teams see your actual work.