Overview What You Will Learn Curriculum Instructor

Course Overview

This comprehensive course is designed for tech support professionals, system administrators (Linux and Windows), application support engineers, cybersecurity specialists, and network support engineers. It aims to equip the learners with the knowledge and skills necessary to leverage Large Language Models (LLMs) and Generative AI tools, enhancing troubleshooting capabilities and automating routine tasks. 

In this course, we will learn the concepts and use of Large Language Models and Generative AI, exploring popular tools such as OpenAI GPT, Google Gemini, and Anthropic Claude. You will learn how to apply these powerful AI models to common technical support scenarios and automate routine tasks, significantly boosting your productivity. A key component of the course is the practical application of AI in support roles. Participants will learn to build simple Retrieval-Augmented Generation (RAG) applications, integrating LangChain and Vector Databases with LLMs to create custom solutions tailored to their specific support needs.  

By the conclusion of this course, participants will have gained the skills to effectively utilize LLM tools for complex troubleshooting, develop AI-powered workflows to increase productivity, and create basic RAG applications for specialized support tasks.  

This course offers a unique opportunity for technical professionals to stay ahead of the curve, embracing the future of AI-assisted technical support and troubleshooting. Participants will emerge with practical, immediately applicable skills that can transform their approach to technical problem-solving and support. 

What You Will Learn

  • Explore key concepts of Generative AI and LLMs and compare model capabilities through hands-on demos
  • Learn to build a simple RAG application with Vector Databases and create effective prompts for resolving technical issues
  • Gain expertise on applying LLMs to improve system and data preparedness, including backup strategies and update procedures
  • Discover how to generate code and commands using LLMs to assist in various support activities
  • Learn how to use LLMs to troubleshoot Windows systems, resolving common issues like performance, security, and application errors
  • Gain knowledge on utilizing LLMs to enhance Linux troubleshooting processes
  • Learn to assess the strengths and limitations of LLMs in technical support and integrate them into workflows to boost efficiency

Program Curriculum

  • Overview of Generative AI
  • Applications and Use Cases
  • What are Large Language Models?
  • Chapter 1 Quiz

  • Popular Large Language Models
  • Multi-modal Models
  • Chapter 2 Quiz

  • Overview of RAG Applications
  • Vector Databases
  • Developing a Simple RAG Application
  • Chapter 3 Quiz

  • Understanding the Components of a Prompt
  • Types of Prompts
  • Demo
  • Chapter 4 Quiz

  • Automating Backup Scripts and Monitoring Backup Status Using LLMs
  • Automating Update Checks and Deployment Using LLMs
  • Automating Data Integrity Checks and Reporting
  • Chapter 5 Quiz

  • Introduction to Automated Code Generation with LLMs
  • Automate a Linux Admin Action with LLM
  • Automating a Windows Admin Action
  • Automating an Application Support Action
  • Chapter 6 Quiz

  • Identifying Windows Issues with LLMs
  • Analyzing Logs for Errors and Remediation Recommendations
  • Chapter 7 Quiz

  • Introducing Custom GPTs
  • Creating a Custom GPT for Linux Support Tasks
  • Chapter 8 Quiz
Load more modules

Instructor

Manas Dasgupta

Manas holds an MSc in AI-ML from Liverpool John Moores University (LJMU), UK. His expertise includes Generative AI (RAG application development using frameworks like LangChain and LlamaIndex), Machine Learning, Data Science, and Predictive Analytics, with a focus on techniques such as Supervised and Unsupervised Learning, Deep Neural Networks, and Clustering.His research focuses on Natural Language Processing (NLP) using Deep Learning methods like Siamese Networks, Encoder-Decoder techniques, and Language Embeddings (e.g., BERT).With over 20 years of experience in IT, primarily in Financial Services, Manas has developed solutions for top banks and institutions, including Natwest, ANZ, KBC, Visa, and Experian.

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