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  3. Leveraging Llama2 for Advanced AI Solutions

Leveraging Llama2 for Advanced AI Solutions

This course equips learners to design, develop, and optimize advanced LLM solutions using LLama2. It covers LLM architectures, fine-tuning, RAG, and tools like LangChain and Hugging Face, offering hands-on experience with cutting-edge AI techniques.

Fabian Hinsenkamp
Fabian Hinsenkamp
Data Science | intermediate | 2 hours |   Published: Jan 2025
In partnership with:  Coursera

    Discussions

Overview

1KSTUDENTS*
95.5%RECOMMEND*

This course includes:

  • 2 hours of on-demand video  
  • Certificate of completion  
  • Direct access/chat with the instructor 
  • 100% self-paced online 

The focus of this course is to equip learners with the skills and knowledge to design, develop, and optimize advanced large language model (LLM) solutions using LLama2. Topics covered will include a comprehensive understanding of LLM architectures, techniques for fine-tuning LLMs, retrieval-augmented generation (RAG), and the utilization of tools like Ollama, LangChain, Streamlit, and Hugging Face. This course will be exciting for learners as it delves into cutting-edge advancements in AI, offering hands-on experience with state-of-the-art tools and techniques. 

Skills You Will Gain

Large Language Models (LLMs)
Retrieval-Augmented Generation (RAG)
Fine-Tuning LLMs
LangChain
Machine Learning Optimization

Learning Outcomes (At the end of this program you will be able to)

  • Evaluate LLMs conceptually and comprehend the solution development process. 
  • Analyze use cases for LLMs and determine Optimal Architectures, Models, and Optimization Techniques.  
  • Apply and compare Diverse Optimization Techniques for LLM Models. 
  • Design and develop Advanced LLM Solutions Utilizing LLama2.

Prerequisites

Learners should have basic Python skills, GitHub and Hugging Face accounts, and a system with at least 8GB RAM, 3.8GB free storage, running macOS or Windows.

Who Should Attend

Software Engineers, Machine Learning Engineer, Data Scientist, Engineering Managers  

Curriculum

Instructors

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Yes, we do offer a 100% refund guarantee for our courses within a specified time frame. If you are not satisfied with the course, contact our customer support team to request a refund with your order details. Some restrictions may apply.

*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.
1Lesson 1: Begin Your LLM Journey: Introduction to Advanced AI Solutions
2Lesson 2: Building a RAG App to Explain Scientific Papers
3Lesson 3: Beyond RAG: Developing a Fine-Tuned Model to Explain Scientific Papers
Fabian Hinsenkamp

Fabian Hinsenkamp

Fabian is an AI/ML Engineer at Microsoft, bringing extensive expertise in system design and architecture to learners through this online teaching platform. With years of experience in one of the tech industry's most innovative environments, he has developed a deep understanding of complex systems and how they can be optimized using artificial intelligence and machine learning.

His journey in technology has been driven by a passion for solving challenging problems and a commitment to continuous learning. At Microsoft, Fabian has worked on cutting-edge projects, collaborated with brilliant minds, and stayed at the forefront of technological advancements. This experience has provided him with valuable insights and practical skills, which he is eager to share with aspiring and experienced professionals alike.

In his courses, Fabian combines theoretical knowledge with real-world applications, offering a comprehensive understanding of system design and architecture in the modern tech landscape. Whether for beginners exploring the field or seasoned professionals looking to enhance their skills, his teaching approach ensures that learning remains engaging, accessible, and highly applicable.

Join Fabian in exploring the fascinating world of AI and ML, and embark on a journey to unlock the potential of technology together.

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RAG Solutions: The Basics and Beyond

New Bringing RAG to Life: Implementing the Solution

Crafting the UI: Making RAG User-Friendly

Fine-Tuning Fundamentals: Key Concepts Explained

Step-by-Step: Preparing and Training Your Model

Final Touches: Bringing Your Fine-Tuned Model to Life

Choosing the Right Path: Fine-Tuning vs. RAG for Your AI Projects

Congratulations and Continuous Learning Journey

Introduction to the Course & Meet Your Instructor

Demystifying LLM Solutions

Tool Time: Download & Install Essentials

LLama2 in Action: Create YOUR First Model