Description

AI and Large Language Models (LLMs) are revolutionizing industries, automating workflows, and enhancing productivity across multiple domains. However, the complexity of AI development often discourages non-technical individuals from exploring this field.

This course is designed for beginners with no coding experience and will provide a step-by-step guide on how LLMs work, how they can be built using no-code tools, and how to fine-tune them for specific tasks. By the end of the course, participants will have hands-on experience creating, fine-tuning, and deploying their own LLM using accessible, no-code platforms

Course Objectives

Understand what LLMs are and how they work (in simple, non-technical terms)

Learn the difference between open-source and proprietary LLMs

Explore the essential steps involved in building an LLM without coding

Learn how to collect and prepare training data for LLMs

Gain hands-on experience using no-code AI platforms to build an LLM

Fine-tune an LLM for specific tasks such as chatbots and text generation

Deploy their custom-trained LLM for real-world use

Target Audience

Non-technical learners – Anyone with no prior coding or AI experience who wants to understand LLMs

Business professionals – Decision-makers looking to integrate AI into their businesses

Content creators & marketers – Individuals exploring AI-driven automation for content generation

Educators & researchers – Those interested in using AI for knowledge sharing and education

AI enthusiasts – Anyone curious about AI and looking for an easy-to-follow introduction

Basic Understanding

This course is designed to be completely beginner-friendly, and no prior AI or programming knowledge is required. However, having a basic understanding of the following will be beneficial:

Basic Computer Skills – Ability to navigate web-based tools and platforms

General Awareness of AI Concepts (Optional) – Helps in understanding real-world applications

No Coding or Machine Learning Experience Needed – The course uses no-code platforms to simplify LLM creation

Course Content

No sessions available.

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Building Your First Large Language Model (LLM) – A Beginner’s Guide (No Coding Required)

Session 1: Understanding the Basics of LLMs

  1. What is an LLM?

    Introduction to AI and Large Language Models

    How LLMs are different from traditional AI systems

    Real-world applications of LLMs

  2. How LLMs Learn and Work (Non-Technical Explanation)

    Explanation of training data, tokens, and embeddings

    Pre-training vs fine-tuning in simple terms

    What makes a model "large"? (Scale, parameters, etc.)

  3. Types of LLMs

    Open-source LLMs (e.g., GPT-based, BERT, DeepSeek)

    Proprietary LLMs (e.g., ChatGPT, Bard)

    Pros and cons of open-source vs proprietary models

Session 2: The Step-by-Step Process to Create an LLM (Non-Coding Approach)

  1. Choosing the Right Approach to Build an LLM

    From scratch vs leveraging existing LLMs (distilled models, pre-trained LLMs)Identifying the purpose of your LLM (e.g., customer support chatbot, text summarizer)

  2. Gathering and Preparing Data for an LLM (Simplified)

    What kind of data is used to train LLMs?

    Sources of training data (text corpora, documents, etc.)

    Importance of clean, diverse, and unbiased data

  3. Understanding the Training Process (No Code)

    How training happens conceptually (explained visually)

    Key components: epochs, loss function, optimization

    Cloud-based and automated LLM training solutions (Hugging Face, Google Colab)

Session 3: Building Your First Custom LLM (Hands-On Walkthrough)

  1. Using a No-Code LLM Tool

    Introduction to no-code platforms for AI (e.g., Hugging Face AutoTrain, Forefront AI, Runway ML)

    Setting up a simple LLM project (guided tutorial)

  2. Fine-Tuning an LLM for a Specific Task (Simplified)

    What is fine-tuning?

    Uploading your dataset to the platform

    Training the LLM and monitoring its performance

  3. Testing and Deploying Your LLM

    Evaluating your model’s performance (accuracy, coherence)

    Deploying your LLM as a chatbot or API with no-code tools

    Use case demo: Creating a simple FAQ bot using your trained LLM

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