Generative Ai (Basic To Advanced)

Learn how Generative AI models are revolutionizing content, code, innovation using prompt engineering & real-world tech.

What you'll learn

Understand what Generative AI is and how it works

Learn different types of generative models (GANs, VAEs, Transformers, etc.)

Explore how GenAI can generate text, images, audio, and video

Build and train basic generative models from scratch

Use pre-trained models like GPT and DALL·E for real-world tasks

Practice prompt engineering to get better results from AI models

Learn how to fine-tune models for specific use cases

Build GenAI applications like chatbots, content creators, and art generators

Understand the risks, ethics, and responsible use of GenAI

Requirements

Enthusiasm and determination to make your mark on the world!

Description

A warm welcome to the Generative AI (basic to advanced) course by Uplatz.Generative AI (Generative Artificial Intelligence) refers to a type of artificial intelligence that is capable of creating new content—such as text, images, audio, code, and more—rather than simply analyzing existing data. It mimics human creativity by learning from large datasets and generating outputs that resemble original, human-made content.What It DoesTraditional AI systems are good at recognizing patterns or making predictions based on existing data. Generative AI goes a step further by actually producing new data that didn't exist before. For example:Writing articles or storiesCreating images or artworkComposing musicWriting codeDesigning products or layoutsHow It WorksGenerative AI typically relies on advanced machine learning techniques, especially deep learning models such as:Transformers – used in models like GPT (text) or T5Diffusion models – used in image generation (like DALL·E or Stable Diffusion)GANs (Generative Adversarial Networks) – used for creating realistic mediaHere’s a simplified breakdown of the process:TrainingThe model is trained on massive datasets (e.g., books, websites, images, code).It learns statistical patterns, styles, and relationships in the data.Learning ProbabilitiesInstead of memorizing, the model learns the probability of what should come next in a sequence (next word, next pixel, etc.).Generation (Inference)When you give it a prompt, it generates new content based on what it has learned.For instance, if you type a sentence, a text model will complete it or write a full article.If you input a concept, an image model can generate an image matching that description.Fine-TuningThe base model can be refined using reinforcement learning or task-specific data to make it more accurate, aligned, or safer.Common Applications of Generative AIText GenerationWriting articles, blogs, and essaysDrafting emails and messagesSummarizing long documentsTranslating languagesAnswering questions or tutoringImage GenerationCreating digital art and illustrationsGenerating product mockups and logosDesigning ads, posters, and visual contentStyle transfer and photo editingCode GenerationAuto-completing codeGenerating boilerplate scriptsFixing bugs and refactoring codeExplaining code snippetsAudio and MusicComposing original musicGenerating voiceovers or speechProducing sound effectsVoice cloning and enhancementVideo GenerationCreating short films and animationsGenerating explainer videosVideo summarizationScene-to-video synthesis3D Modeling and DesignGenerating 3D objects and environmentsDesigning virtual products or architectureGame asset creationGamingProcedural content and level generationNPC (non-player character) behavior scriptingDialogue generationFashion and Product DesignDesigning apparel and accessoriesCreating virtual try-onsGenerating custom product variantsEducationPersonalized tutoring and explanationsQuiz and flashcard generationAdaptive learning contentMarketing and AdvertisingWriting ad copy and taglinesCreating personalized campaignsDesigning social media postsLegal and ComplianceDrafting legal documentsReviewing and summarizing policiesIdentifying contract risksHealthcare and BiotechGenerating radiology and diagnostic reportsSimulating molecular structuresSummarizing patient recordsCustomer SupportChatbots for FAQs and ticket handlingEmail and chat summarizationResponse recommendationFinanceAutomating financial reportsAnalyzing and summarizing earnings callsDetecting unusual financial patternsBenefitsRapid content generationPersonalized or on-demand outputsAutomation of creative and technical tasksSupport for brainstorming and ideationTime and cost efficiency for businessesChallenges and RisksMay generate incorrect or misleading contentCan reflect biases from the training dataRisk of misuse for fake content or misinformationComputational and environmental costsRequires careful monitoring and human validation

Overview

Section 1: Large Language Models (LLMs)

Lecture 1 Part 1 - Large Language Models (LLMs)

Lecture 2 Part 2 - Large Language Models (LLMs)

Lecture 3 Part 3 - Large Language Models (LLMs)

Lecture 4 Part 4 - Large Language Models (LLMs)

Lecture 5 Part 5 - Large Language Models (LLMs)

Lecture 6 Part 6 - Large Language Models (LLMs)

Lecture 7 Part 7 - Large Language Models (LLMs)

Lecture 8 Part 8 - Large Language Models (LLMs)

Lecture 9 Part 9 - Large Language Models (LLMs)

Lecture 10 Part 10 - Large Language Models (LLMs)

Lecture 11 Part 11 - Large Language Models (LLMs)

Lecture 12 Part 12 - Large Language Models (LLMs)

Lecture 13 Part 13 - Large Language Models (LLMs)

Lecture 14 Part 14 - Large Language Models (LLMs)

Lecture 15 Part 15 - Large Language Models (LLMs)

Lecture 16 Part 16 - Large Language Models (LLMs)

Lecture 17 Part 17 - Large Language Models (LLMs)

Lecture 18 Part 18 - Large Language Models (LLMs)

Lecture 19 Part 19 - Large Language Models (LLMs)

Lecture 20 Part 20 - Large Language Models (LLMs)

Lecture 21 Part 21 - Large Language Models (LLMs)

Section 2: Coding of Transformers and LLMs

Lecture 22 Part 1 - Coding of Transformers and LLMs

Lecture 23 Part 2 - Coding of Transformers and LLMs

Lecture 24 Part 3 - Coding of Transformers and LLMs

Lecture 25 Part 4 - Coding of Transformers and LLMs

Lecture 26 Part 5 - Coding of Transformers and LLMs

Lecture 27 Part 6 - Coding of Transformers and LLMs

Lecture 28 Part 7 - Coding of Transformers and LLMs

Lecture 29 Part 8 - Coding of Transformers and LLMs

Lecture 30 Part 9 - Coding of Transformers and LLMs

Lecture 31 Part 10 - Coding of Transformers and LLMs

Lecture 32 Part 11 - Coding of Transformers and LLMs

Lecture 33 Part 12 - Coding of Transformers and LLMs

Lecture 34 Part 13 - Coding of Transformers and LLMs

Lecture 35 Part 14 - Coding of Transformers and LLMs

Lecture 36 Part 15 - Coding of Transformers and LLMs

Lecture 37 Part 16 - Coding of Transformers and LLMs

Lecture 38 Part 17 - Coding of Transformers and LLMs

Lecture 39 Part 18 - Coding of Transformers and LLMs

Lecture 40 Part 19 - Coding of Transformers and LLMs

Lecture 41 Part 20 - Coding of Transformers and LLMs

Lecture 42 Part 21 - Coding of Transformers and LLMs

Lecture 43 Part 22 - Coding of Transformers and LLMs

Lecture 44 Part 23 - Coding of Transformers and LLMs

Lecture 45 Part 24 - Coding of Transformers and LLMs

Lecture 46 Part 25 - Coding of Transformers and LLMs

Lecture 47 Part 26 - Coding of Transformers and LLMs

Lecture 48 Part 27 - Coding of Transformers and LLMs

Lecture 49 Part 28 - Coding of Transformers and LLMs

Lecture 50 Part 29 - Coding of Transformers and LLMs

Lecture 51 Part 30 - Coding of Transformers and LLMs

Lecture 52 Part 31 - Coding of Transformers and LLMs

Lecture 53 Part 32 - Coding of Transformers and LLMs

Lecture 54 Part 33 - Coding of Transformers and LLMs

Lecture 55 Part 34 - Coding of Transformers and LLMs

Lecture 56 Part 35 - Coding of Transformers and LLMs

Lecture 57 Part 36 - Coding of Transformers and LLMs

Lecture 58 Part 37 - Coding of Transformers and LLMs

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Lecture 60 Part 39 - Coding of Transformers and LLMs

Lecture 61 Part 40 - Coding of Transformers and LLMs

Lecture 62 Part 41 - Coding of Transformers and LLMs

Section 3: Prompt Engineering for Generative AI

Lecture 63 Part 1 - Prompt Engineering for Generative AI

Lecture 64 Part 2 - Prompt Engineering for Generative AI

Lecture 65 Part 3 - Prompt Engineering for Generative AI

Lecture 66 Part 4 - Prompt Engineering for Generative AI

Lecture 67 Part 5 - Prompt Engineering for Generative AI

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Lecture 70 Part 8 - Prompt Engineering for Generative AI

Lecture 71 Part 9 - Prompt Engineering for Generative AI

Lecture 72 Part 10 - Prompt Engineering for Generative AI

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Lecture 74 Part 12 - Prompt Engineering for Generative AI

Lecture 75 Part 13 - Prompt Engineering for Generative AI

Lecture 76 Part 14 - Prompt Engineering for Generative AI

AI/ML Engineers – looking to build or fine-tune generative models,Software Developers – wanting to integrate GenAI into applications,Data Scientists – interested in advanced modeling and content generation,Tech Enthusiasts & Hobbyists – curious about AI tools like ChatGPT, Midjourney, DALL·E,Product Managers – aiming to understand capabilities and limitations of GenAI,Startup Founders & Innovators – exploring GenAI use cases and MVP development,Content Creators & Designers – who want to leverage AI for creative work,Researchers & Academics – studying generative models and their applications,Business Professionals – interested in using AI to improve workflows and automation,Students & Career Changers – who want to enter the AI field with hands-on GenAI skills