Bridging the gap from ML basics to advanced LLMs
What you'll learn
Leveraging traditional ML knowledge for working with LLMs
Hands-on experience with PyTorch for LLMs
Deeply understand the details of Transformer architecture
Unfold the use-cases of LLMs for different tasks
Discover new evaluation metrics specifically for LLMs
Perform Text Classification and Text Summarization in Python
Get familiar with concepts like RLHF or OpenAI API
Confidence to make the first steps in LLMs
Requirements
Knowledge of traditional ML basic concepts and Python
Description
Unlock the most recent 'now' of machine learning with this hands-on, fast-paced crash course entitled "From Traditional ML to LLMs."Your Story:[Hypothetical] Anna, a seasoned ML engineer, had mastered traditional machine learning models, but every job listing screamed "LLMs." The world was moving on, and she needed to keep up. Learning Large Language Models sounded like a daunting leap—until she found a way to bridge her existing skills with the cutting-edge techniques she needed. This course was her solution.[Hypothetical] Jamal was a data scientist with strong ML experience, but transformers and tokenization seemed like a different universe. He needed to add LLMs to his skill set to stay competitive, and he didn't want theory; he wanted practical, hands-on applications that would help him shine in real-world projects.My Story: I've been where you are—armed with traditional ML knowledge but looking to level up. I struggled with endless tutorials and theories, but through persistence, I got hands-on and found the perfect way to apply my traditional ML expertise to LLMs. I went from logistic regression models to transformer-based LLMs, and now I want to help you do the same. By the end of this course, you'll confidently build and fine-tune LLMs using your existing knowledge, apply PyTorch, and solve real-world text-based challenges.What You'll Learn: In this course, I won't just throw theory at you. You'll gain real, actionable skills to bridge the gap from traditional ML to LLMs, helping you tackle practical challenges in the industry. Here's what you'll get:Core skills refreshed and connected to LLMs.A deep understanding of the famous Transformers.Practical insights into LLM concepts — from tokenization to RLHF.A hands-on project-based approach where you'll build a text classification and a summarization model using PyTorch.How This Course is Structured: I know learning LLMs can feel like stepping into a foreign world. So, I've designed this course to be practical and fun—no abstract concepts, just real-world applications. I'll walk you through exercises and examples based on actual ML-to-LLM workflows. Expect quizzes and assignments that you can apply directly to your work.FAQs:Do I need to know LLMs already? - Nope! We'll cover everything you need from basic architecture concepts to advanced LLMs.Will this course work for PyTorch beginners? - Absolutely! We guide you through the necessary steps to build and fine-tune your first models.Ready to close the gap between traditional ML and the next wave of AI innovation? Jump in and let's get started!
Who this course is for
Data scientists with good background in traditional ML but lacking any knowledge in LLMs

https://rapidgator.net/file/d102677af1c86bcaf83f63056e20fcc9/From_Traditional_ML_to_LLMs.rar.html