Generative AI Course

Embark on an innovative journey into the realm of generative AI with our specialized course. Explore cutting-edge techniques in generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. Our hands-on curriculum ensures you gain practical experience in building generative models for diverse applications. Join us to unlock the creative potential of AI and shape the future of generative technologies.

|| What will I learn?

  • Understand the fundamental concepts and techniques of generative AI.
  • Implement and train various generative models using Python and deep learning frameworks.
  • Apply generative AI techniques to create new images, text, and other forms of content.
  • Evaluate the performance and quality of generative models.
  • Explore advanced topics in generative AI, such as style transfer and latent space manipulation.
  • Understand the ethical implications and societal impacts of generative AI.

|| What will I learn?

  • Understand the fundamental concepts and techniques of generative AI.
  • Implement and train various generative models using Python and deep learning frameworks.
  • Apply generative AI techniques to create new images, text, and other forms of content.
  • Evaluate the performance and quality of generative models.
  • Explore advanced topics in generative AI, such as style transfer and latent space manipulation.
  • Understand the ethical implications and societal impacts of generative AI.

|| Requirements

  • Proficiency in Python programming
  • Basic understanding of machine learning and deep learning concepts
  • Familiarity with neural networks and deep learning frameworks (TensorFlow, PyTorch)

|| Requirements

  • Proficiency in Python programming
  • Basic understanding of machine learning and deep learning concepts
  • Familiarity with neural networks and deep learning frameworks (TensorFlow, PyTorch)

    • Generative AI
    • Why are generative models required?
    • Understanding generative models and their significance
    • Generative AI v/s Discriminative Models
    • Recent advancements and research in generative AI
    • Gen AI end-to-end project lifecycle
    • Key applications of generative models


    • Text Preprocessing and Word Embedding
    • Segmentation and Tokenization
    • Change Case, Spell Correction
    • Stop Words Removal, Punctuations Removal, Remove White
    • spaces, Stemming and Lemmatization
    • Parts of Speech Tagging
    • Text Normalization, Rephrase Text
    • One hot encoding, 
    • Index-based encoding
    • Bag of words, 
    • TF-IDF
    • Word2Vec, 
    • FastText
    • N-Grams, Elimo
    • Bert-based encoding


    • Large Language Models(LLM)
    • In-depth intuition of Transformer-Attention all your need Paper
    • Guide to complete transformer tree
    • Transformer Architecture
    • Application and use cases of LLMs
    • Transfer learning in NLP
    • Pre-trained transformer-based models
    • How to perform finetuning of pre trained transformer based models
    • Mask language modeling


    • BERT- Google, GPT- OpenAI
    • T5- Google
    • Evaluations Matrixs of LLMs models
    • GPT-3 and 3.5 Turbo use cases
    • Learn how Chatgpt trained
    • Introduction to Chatgpt- 4


    • Hugging face And its Applications
    • Hugging Face Transformers
    • Hugging face API key generation
    • Hugging Face Transfer learning models based on the state-of-the-art transformer architecture
    • Fine-tuning using a pre-train models
    • Ready-to-use datasets and evaluation metrics for NLP.
    • Data Processing, Tokenizing and Feature Extraction with
    • Standardizing the Pipelining
    • Training and callbacks
    • Language Translation with Hugging Face Transformer


    • Generative AI with LLMs and LLM Powered Applications
    • Text summarization with hugging face
    • Language Translation with Hugging Face Transformer
    • Text to Image Generation with LLM with hugging face
    • Text to speech generation with LLM with hugging face


    • Guide to Open AI and its Ready to Use Models with Application
    • What is OpenAI API and how to generate OpenAI API key?
    • Installation of OpenAI package
    • Experiment in the OpenAI playground
    • How to setup your local development environment
    • Different templates for prompting
    • OpenAI Models GPT-3.5 Turbo DALL-E 2, Whisper, Clip,
    • Davinci and GPT-4 with practical implementation
    • OpenAI Embeddings and Moderation with Practical
    • Implementation of Chat completion API,


    • Functional calling and Completion API
    • How to manage the Tokens
    • Different Tactics for getting an Optimize result
    • mage Generation with OpenAI LLM model
    • Speech to text with OpenAI
    • Use of Moderation for content complies with OpenAI
    • Understand rate limits, error codes in OpenAPI
    • OpenAI plugins connect ChatGPT to third-party applications.
    • How to do fine-tuning with custom data
    • Project: Finetuning of GPT-3 model for text classification
    • Project: Telegram bot using OpenAI API with GPT-3.5 turbo
    • Project: Generating YouTube Transcript with Whisper
    • Project: Image generation with DALL-E
    • Prompt Engineering Mastering with OpenAI


    • Introduction to Prompt Engineering
    • Different templates for prompting
    • Prompt Engineering: What & Why?
    • Prompt Engineering & ChatGPT Custom Instructions
    • The Core Elements Of A Good Prompt
    • Which Context Should You Add?
    • Zero- One- & Few-Shot Prompting
    • Using Output Templates
    • Providing Cues & Hints To ChatGPT
    • Separating Instructions From Content
    • Ask-Before-Answer Prompting
    • Perspective Prompting
    • Contextual Prompting
    • Emotional Prompting
    • Laddering Prompting
    • Using ChatGPT For Prompting
    • Find Out Which Information Is Missing
    • Self-evaluative Prompting
    • ChatGPT-powered Problem Splitting
    • Reversing Roles
    • More Prompts & Finding Prompt Inspirations
    • Super Prompts Like CAN & DAN


    • Vector database with Python for LLM Use Cases
    • Storing and retrieving vector data in SQLite
    • Chromadb local vector database part1 setup and data insertion
    • Query vector data
    • Fetch data by vector id
    • Database operation: create, update, retrieve, deletion, insert and
    • update
    • Application in semantic search
    • Building AI chat agent with langchain and openai
    • Weviate Vector Database
    • Pinecone Vector Database


    • Hands-on with LangChain
    • Practical Guide to LlamaIndex with LLMs
    • Bonus: Additional Productive Tools to Explore
    • Chainlit ( async Python framework)
    • LIDA (Automatic Generation of Visualizations and
    • Infographics)
    • Slidesgo ( AI Presentation Maker )
    • Content Creation (Jasper, Copy.ai, Anyword)
    • Grammar checkers and rewording tools (Grammarly, Wordtune,
    • ProWritingAid)
    • Video creation (Descript, Wondershare Filmora, Runway)
    • Image generation (DALL·E 2, Midjourney)
    • Research (Genei, Aomni)

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|| Frequently asked question

A Generative AI Course focuses on teaching participants the theory, algorithms, and applications of generative artificial intelligence (AI), which involves creating models that can generate new content, such as images, text, audio, and videos, based on patterns learned from existing data.

This course is suitable for students, professionals, and enthusiasts interested in exploring the field of generative AI. It caters to individuals with varying levels of experience, from beginners with no prior knowledge to experienced practitioners looking to specialize in generative modeling techniques.

Prerequisites may include a basic understanding of machine learning concepts, proficiency in a programming language such as Python, familiarity with linear algebra, calculus, and probability theory, and prior experience with neural networks or deep learning frameworks.

Most reputable Generative AI courses offer a certificate of completion, which can enhance your credentials and be added to your resume or LinkedIn profile. It's essential to verify the accreditation and recognition of the issuing institution or organization.

Some courses offer job placement assistance or career services, including resume building, interview preparation, and networking opportunities with industry professionals. However, this varies depending on the course provider and the course's focus.

Yes, many Generative AI courses are available online, offering flexibility in terms of timing and location. Online courses often provide video lectures, interactive exercises, and discussion forums to facilitate learning.

Yes, Generative AI courses typically include hands-on projects, case studies, and practical exercises to apply the techniques learned to real-world problems. This practical experience is essential for developing proficiency and building a portfolio of projects.

After completing the course, you may continue to have access to course materials, online resources, alumni networks, career services, and professional development opportunities to support your continued learning and career growth.

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