Certified LLM Developer Course For Beginners

Introduction

The Certified LLM Developer course is perfect for beginners who want to learn about Large Language Models (LLMs), how they work, and what they are. The certification is specially designed for developers interested in learning about the technology behind it and getting access to the wealth of knowledge behind the tools, architecture, and best practices to build these AI models. Through an immersive learning experience, the instructors will teach the participants and give them hands-on experience to create context-aware, intelligent applications using AI.

What Will You Gain from This Course?

Following the completion of this course, the students will be able to:

1. Learn about Large Language Models, their limitations and applications.
2. Understanding the core Large Language Model technologies and advanced techniques deployed to create applications using AI.
3. Get complete knowledge of tools and frameworks used in Large Language Models.
4. Become an expert in LLM architecture and tools.

Skills Acquired

1. Large Language Models Expertise
2. Artificial Intelligence (AI)
3. MLOps
4. Flask
5. Flask API

Who Will Benefit from This Course?

The course is designed for:

1. Individuals interested in becoming an expert in Large Language Models.
2. Professionals who want to enhance their careers in applied natural language processing.
3. Professionals working as software developers, AI engineers or developers, data scientists, and machine learning experts.
4. AI enthusiasts who want to learn more about how Large Language Models work and how machine learning and AI work in this natural language processor.

Course Content

12 Modules – Certificate of Completion

Introduction to Large Language Models

The first module of this course will give an overview of large language models (LLM). The instructor will take the students through the evolution of LLMs before discussing their limitations and capabilities. To conclude the first module, the instructor will discuss the use cases and applications of LLMs.

Core LLM Technologies

The second module of this course will teach the students about the fundamental concepts of large language models. They will start the module with vectors, embeddings, and tokenizations. When these concepts are clear, they will learn about Attention Machanism and its variants. After that, the participants will be introduced to Transformer Architecture and the creation of custom language models. The students will then get to know about transfer learning in natural language processing as well as the evolution metrics for LLMs such as ROUGE, BLEU, and Perplexity. Finally, to end this extensive module, the participants will also learn about llama2 and Gemma, along with ways to fine-tune Gemma models.

Advanced LLM Techniques

The third module in this course will begin with an overview of the popular LLMs we use today, such as BERT, GPT-3/4, and T5. The participants will then jump right into finetuning pre-trained models for specific tasks. The module will then focus on different LLMs. It will look extensively into BERT and its variants, such as RoBERTa and DistilBERT. It will also look into GPT and its applications in text generation. The instructor will also talk about exploring other models, such as ELECTRA, XLNet, and T5.

The course will become technical as the participants will get to learn about building conversational chatbots and agents. The students will also learn about creative applications like storytelling and text generation. The module will end with bias mitigation in LLMs and ethical considerations.

Computer Vision

The fourth module of the Certified LLM Developer course will discuss computer vision. The instructor will start the session by helping the students understand computer vision. After that, the participants will learn about CNN from scratch and CNN using Tensorflow.

Audio/Video Coding

In the fifth module of the course, the students will learn about the basics of audio signal processing. They will also build their knowledge about feature extractions such as MFCCs and Spectrograms. The students will also learn about speech recognition and audio classification. After that, the focus of the course will shift to the basics of video signal processing. The participants will also learn about video feature analysis as well as frame extraction.

LLM Frameworks and Tools

In this module, the students will get up close and personal with different LLM frameworks and tools. First, the participants will learn about the LangChain framework. They will learn about using LangChain for conversational AI applications, deploying language model APIs, and RAG workflows. Lastly, the instructor will teach the students about the Ollama tool. The sessions on Ollama will focus on an overview of Ollama for conversational AI as well as developing and deploying conversational agents with Ollama.

Project 1: Text Classification Model

In this module, the students will first get an overview of the text classification model. After that, they will be exposed to BERT text classification, how they should prepare data and process it, and learn about the text generation model.

Project 2: Text Generation Model

The students will get an understanding of what the text generation model is and how they can evaluate and finetune it.

Project 3: Designing a Conversational Agent Architecture

In the third part of the project module series, the students will learn about designing a conversational agent architecture. They will know about what a conversational agent is and how they can create it using OpenAI, LangChain, Ollama, and HuggingFace.

Deployment and MLOps

As the course reaches its end, the participants will learn about deploying LLMs with FastAPI and Flask. They will get an introduction to Docker for containerization as well as learn how they can deploy LLMs on cloud platforms like AWS. The instructor will give an introduction to MLOps, its core concepts, and best practices. The module will look into continuous integration and continuous deployment. The module will end with learning how to monitor model performance in production.

Recommended Learning Methodology

It is recommended that the students give at least one hour daily to the course so that they can finish it in fifteen days. While they can attempt the exam at their convenience, it is recommended that they attempt the exam within ten days of finishing the course.

Exam

To get the certification, the students will have to appear for an exam with 100 marks in total. They need to achieve sixty-plus marks to clear the exam. If the students fail to gain these passing marks, they may retake the exam after one day and can retake it up to three times.

Description

The Certified LLM Developer course has been designed to help developers learn about large language models so they can become experts in the technology. The students will get access to lectures and sessions by industry experts as they learn about building, fine-tuning, and perfecting their own large language model. By the end of this course, the participants will have complete knowledge of the tools, architecture, and best practices to build these AI models.

Meet the Instructor

Blockchain Council

Blockchain Council is one of the leading blockchain platforms where students can get to learn about AI, blockchain, machine learning, and more from experts. Founded in 2017, the Blockchain Council has imparted knowledge to millions of professionals and students who want to further their careers with market-intuitive skills. The Blockchain Council conducts meetings, online webinars, training sessions, seminars, and events as well. The council aims to help individuals and organizations with blockchain skills so they can contribute to the future.