Learn how to build, fine-tune, and deploy advanced large languages models like GPT, BERT, RoBERTa, and more. Combine project-based learning with practical PyTorch implementation to master generative AI concepts.
Generative AI – Large Language Models
Who Should Take This Course?
This course is designed for individuals eager to unlock the potential of generative AI:
- Data Engineers and developers looking to advance their skill set by mastering large language models and applying them to real-world projects.
- AI enthusiasts and analysts seeking to transition into roles focused on machine learning or artificial intelligence.
- Professionals and team leaders aiming to leverage models like GPT and BERT to drive innovation and efficiency in their workflows.
- Those curious about generative AI, whether you’re just starting out or ready to take your AI knowledge to the next level.
If working hands-on with cutting-edge AI tools excite you, this course will guide you every step of the way.
What You’ll Need to Get Started
This course doesn’t require deep AI expertise but works best if you have some basics in place. Familiarity with Python programming will make it easier to work through implementations using PyTorch. Knowing a bit about machine learning fundamentals will also give you a head start, although it’s not a strict requirement.
What matters most is your curiosity and readiness to learn. If you’re excited about exploring how large language models like GPT or BERT function and eager to build practical solutions, you’re already set to thrive. We’ll provide the step-by-step guidance you need to succeed.
What You’ll Learn
This course is designed to take you from foundational concepts in transformers to mastering advanced LLMs step by step. Every module blends theory with hands-on practical applications to ensure you confidently implement cutting-edge techniques. Detailed breakdown of what you’ll learn:
Module 1: The Basics of Transformers
Transformers are at the core of modern AI models, powering everything from language understanding to text generation. In this module, you’ll build a strong foundation and learn:
- Understand why transformers are fundamental to modern AI, the challenges they solve, and their role in revolutionizing machine learning models.
- Transformer architecture in detail. Explore how the components like encoder-decoder frameworks, tokenization, and positional embeddings work together.
- The power of self-attention. Discover why attention mechanisms are critical to transformers’ success in processing sequential data efficiently.
By the end of this module, you will create a sentiment analysis system from scratch using PyTorchGenerative AI. You will learn how to preprocess data, build a transformer model, and evaluate its performance.
Module 2: Understanding BERT and Its Variants
Take a deep dive into BERT (Bidirectional Encoder Representations from Transformers) and its transformative impact on NLP. This module focuses on:
- How BERT works and its bidirectional capabilities. Understand why looking at text from both directions improves language understanding.
- Hands-on with the BERT tokenizer. Learn how to tokenize datasets and leverage the pre-trained BERT model for real-world tasks.
- Fine-tuning BERT. Gain practical experience adapting BERT to domain-specific applications via fine-tuning techniques.
Build a Named Entity Recognition (NER) model with BERT to identify and classify critical entities (e.g., names, dates) from text, helping you master one of NLP’s most practical tasks.
Module 3: Text Summarization with RoBERTa
RoBERTa enhances BERT’s capabilities, making it a powerful transformer for advanced NLP applications. This module will guide you through:
- Understanding RoBERTa’s key improvements over BERT. Learn how optimized hyperparameters and additional training improve accuracy.
- Using RoBERTa with Hugging Face. See how tools like Hugging Face make working with RoBERTa more intuitive and flexible.
- Text summarization techniques. Explore how to process long documents and distill key information into concise summaries.
Create a text summarization pipeline with RoBERTa, applying RoBERTa’s pre-trained model to generate meaningful summaries from large datasets.
Module 4: GPT Concepts and Applications
GPT models (Generative Pre-trained Transformers) are renowned for their language generation capabilities. In this module, you’ll:
- Learn how GPT generates text. Explore the attention mechanisms (cross and causal) that make GPT excel at coherent text generation.
- Conduct domain-specific experiments. Experiment with prompts to see how GPT adapts to different data in real time.
- Fine-tune GPT for custom use cases. Understand how to optimize GPT for your specific needs, from business workflows to creative text generation.
Fine-tune a GPT model for domain-specific tasks, such as generating business insights or personalized customer communication.
Module 5: Advanced Optimization with T5 and Knowledge Distillation
T5 introduces a flexible Text-to-Text Transfer Transformer framework for any NLP task. You’ll also learn how to make models lighter and faster for deployment:
- Discover how T5 simplifies translation, summarization, and other tasks through its unified text-to-text format.
- Learn how DistilBERT uses distillation techniques to make models more efficient while maintaining their accuracy.
- Gain experience implementing these concepts for scalable applications.
Fine-tune a T5 model for advanced Named Entity Recognition (NER) workflows, creating dependable and high-performing systems for business-critical text tasks.
Real-World Projects You’ll Build
This course ensures you apply what you’ve learned to real-world AI scenarios. Each project is designed to give you practical, hands-on experience:
Sentiment Classifier with Transformers
Learn to build and deploy a PyTorch-based model that processes text data to classify sentiment, helping you understand the full pipeline—from data preparation to evaluation.
Named Entity Recognition Using BERT
Fine-tune BERT to extract structured information like names, locations, and monetary values from unstructured text formats, a skill in high demand for AI-driven workflows.
Text Summarization with RoBERTa
Simplify long, complex documents into concise summaries. You’ll use RoBERTa to create an efficient summarization pipeline applicable to business and research tasks.
Fine-Tuned GPT Model for Business Applications
Transform GPT into a domain-specific tool by fine-tuning it for text generation tasks. Whether for writing prompts, generating responses, or analyzing text patterns, this project sharpens your customization skills.
Entity Recognition with T5
Master T5 by applying advanced fine-tuning techniques to create high-performance models for Named Entity Recognition. These models are perfect for automating workflows that involve extracting entities from large datasets.
Every project is crafted to connect course concepts with real-world AI applications, preparing you to solve business challenges across industries with confidence.
Frequently asked questions
Haven’t found what you’re looking for? Contact us at [email protected] — we’re here to help.
Is this course suitable for beginners?
This course is designed to be accessible to beginners, particularly those transitioning into a data engineering career. While we cover fundamental Python concepts, we prioritize practical data engineering skills.
What tools and technologies will I learn in the course?
You will learn about Python, Pandas, Numpy, SQL, Apache Airflow, Apache Spark, AWS, Azure, Docker, and other data engineering tools and technologies relevant to building and managing data pipelines.
How does this course help me in my career as a data engineer?
The course equips you with practical, industry-relevant skills that are highly sought after in the field of data engineering. You’ll learn how to build data pipelines, manage databases, and work with cloud platforms, which are critical skills for advancing to senior data engineering roles.
Do I need any specific software or hardware for the course?
You will need a computer with an internet connection. The course will guide you through the installation of any required software, such as Python, Jupyter Notebook, and other data engineering tools.