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Fine-Tuning LLMs with Feature Engineering

Exploring Data Inspection, Data Cleaning, and Feature Selection

Recording available until 01. March 2025

Days
Hours
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Seconds
Fine-Tuning LLMs with Feature Engineering
Fine-Tuning LLMs with Feature Engineering

Join our hands-on workshop to master feature engineering essentials. Learn how to inspect, clean, and select the features that truly impact your model’s success. Through a series of practical exercises, we’ll cover:

  • Data Inspection and Cleaning: Start with clean, meaningful data.
  • Feature Engineering: Extract critical features.
  • Feature Selection: Choose the optimal features to enhance model performance and reduce overfitting.

Master Data Preparation for Superior ML Models

Get a taste with the program

This interactive workshop includes guided exercises and expert support at every step. You’ll work in Jupyter Notebooks using essential tools like Pandas, sklearn, and NumPy. With minimal coding required, even participants with limited Python experience will find this workshop accessible and rewarding.

  • Workshop Format: Exercises in Jupyter Notebooks (URL provided) with individual guidance.
  • Minimal Coding: Only essential code (1-3 lines per exercise) needed for key concepts.
  • Real-Life Applications: Apply concepts immediately, with access to source code post-workshop.
Fine-Tuning LLMs with Feature Engineering

You should join:

If you have a basic understanding of machine learning and a bit of programming experience, this workshop is for you. You’ll only need a laptop and an internet connection—no prior Python experience required.

Prepare to elevate your ML model-building skills and start applying feature engineering techniques to real data!

Fine-Tuning LLMs with Feature Engineering

Get to know our expert

Pieter Buteneers

Dr. Pieter Buteneers – CTO Structize AI

Pieter Buteneers is a serial entrepreneur with a PhD in machine learning. As founder and CTO of his latest company, Structize AI, he works to fully automate the M&A due diligence process.

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