Generative AI

Generative AI Course – Master AI Models for Content Creation & Automation

Deep Learning, Generative Course in Nepal

Build AI that creates text, images, and more — Nepal's most affordable Generative AI training.

If you're searching for the best Generative AI course in Nepal, this is it. Code IT's 1-month program goes far beyond prompting AI tools — you'll build, train, and deploy real generative AI systems using TensorFlow, PyTorch, GANs, VAEs, Transformers, GPT-2 via Hugging Face, StyleGAN, DCGAN, and Stable Diffusion — the actual technology powering the AI revolution.

Generative AI is already transforming businesses across Nepal — automating content, accelerating design, and building intelligent applications. The professionals who understand how to build and fine-tune these models, not just use them, are the most in-demand people in Nepal's emerging AI economy. This course puts you in that position.

You'll master deep learning fundamentals with TensorFlow and PyTorch, build your first GAN from scratch, implement LSTM text generation, construct a mini Transformer architecture, generate real text with GPT-2, synthesize images with StyleGAN and DCGAN, and apply Stable Diffusion professionally — all hands-on, all project-based.

Live classes run from 8:00 PM to 9:30 PM via Google Meet, accessible from Kathmandu, Pokhara, Biratnagar, Butwal, Chitwan, or anywhere in Nepal. Classroom sessions available in Dharan. Basic Python and neural network knowledge required.

Every student receives lifetime video access and an industry-recognized certificate from Code IT.

Build AI that creates text, images, and more — Nepal's most affordable Generative AI training.

Every student receives lifetime video access and an industry-recognized certificate from Code IT.

Prerequisites

Basic knowledge of Python machine learning concepts and understanding of neural networks and deep learning fundamentals

Generative AI

Next batch starting soon

Mode: Online (Google Meet)

Duration: 1 month

Rs.2,499/-

Rs.30,000 Save 91%

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Watch Demo Video

Have any Question?

WhatsApp: 9862130505

Telephone: 025-575163

Everything You Receive

All-inclusive support — from training to real-world experience

Live Classes

Google Meet
8:00 PM - 9:30 PM

Lifetime Videos

Re-watch anytime

Certification

Industry recognized

Internship

No internships are available right now.

Course Curriculum

Everything you'll learn — from fundamentals to advanced concepts

Course Outlines

Generative AI Fundamentals
Text Generation & Language Models
Image Generation & Creative AI
Projects & Real-World Applications

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Full Curriculum

01 Introduction to Generative AI

Overview of AI: What is AI?

What is Generative AI?

-- Text generation

--Image synthesis

--Music creation

Applications and Impact of Generative AI

-- Examples in various industries (art, healthcare, etc.)

02 Basics of Machine Learning and Data Preparation

Machine Learning Overview

--Supervised vs Unsupervised Learning

-- Neural networks

Data Preparation and Preprocessing

-- Cleaning and preprocessing data

-- Splitting datasets (train, validation, test)

-- Normalization and standardization

Hands-on: Preprocess the MNIST dataset and train a simple feedforward network using TensorFlow/PyTorch.

03 Gradient Descent and Backpropagation

How neural networks learn

-- Cost functions, gradients, and optimization

-- Gradient Descent

-- Backpropagation

Hands-on: Implement gradient descent for a simple network

04 Deep Learning Frameworks

Overview of TensorFlow and PyTorch

Hands-on: Set up TensorFlow or PyTorch

-- Create a basic neural network.

05 Training and Validation

Overfitting, underfitting, and regularization techniques

Hyperparameter tuning

Hands-on: Train a model on the MNIST dataset.

06 Introduction to Generative Models

What are generative models?

-- Autoencoders, Variational Autoencoders (VAEs), GANs, Transformers

-- Discriminative vs Generative models

Hands-on: Build a simple autoencoder.

07 Hands-on: Build a simple autoencoder.

How GANs work: Generator and discriminator interplay

Hands-on: Generate simple images using a GAN (MNIST dataset).

Recap with a quiz to reinforce concepts.

08 NLP Basics and Language Models

Tokenization, embeddings, and sequence-to-sequence models

Introduction to LSTM and RNNs (limitations)

Pre-trained embeddings (e.g., Word2Vec, GloVe)

Hands-on: Generate text using an LSTM model and integrate pre-trained embeddings.

09 Transformer Models

Key concepts: Self-attention, encoder-decoder architecture

Hands-on: Build a mini-transformer for text generation.

10 GPT Models and Text Generation

Introduction to GPT-2 and GPT-3

Hands-on: Use Hugging Face to generate coherent text with GPT-2

Optional: Fine-tune GPT-2 on a small custom dataset.

11 Ethical Considerations in Text Generation

Biases in generative models

Case studies on ethical issues and mitigation strategies.

12 Image Synthesis Basics

Introduction to Convolutional Neural Networks (CNNs)

Overview of GAN-based image generation

Hands-on: Generate digit images using DCGAN.

Basics of Image Augmentation (flipping, cropping, rotation).

13 Advanced GANs

Conditional GANs (cGANs) and StyleGANs

Hands-on: Modify image styles using StyleGAN.

14 Diffusion Models and DALL-E

Basics of diffusion models

Role in image generation

Introduction to DALL-E and text-to-image models

Hands-on: Generate images with DALL-E or an open-source equivalent.

15 Discussion and Review

Compare GANs, Diffusion models, and Transformers

Real-world applications for each.

16 introduction to LangChain and Document Handling

What is LangChain?

--Benefits for generative AI applications

Basics of vector embeddings

-- What are vector embeddings?

-- How they are generated and stored in vector databases

Hands-on

-- Generate embeddings using a pre-trained model and visualize them

-- Build a pipeline to load and preprocess documents using LangChain.

17 Retrieval-Augmented Generation (RAG)

Understanding RAG: Combining retrieval and generation for improved accuracy

Hands-on

-- Set up a vector database (e.g., FAISS, Pinecone)

--Build a chatbot that retrieves relevant info from documents

18 Capstone Project with LangChain and RAG

Students integrate LangChain and RAG into their capstone projects

-- Example: Build a domain-specific chatbot or knowledge assistant.

19 Deployment of LangChain Applications

Deploy RAG-powered applications using Streamlit or Flask

Hands-on: Create a simple web app for the chatbot or document assistant.

20 Feedback, Iteration, and Final Presentations

Gathering and incorporating feedback into projects

Iterative improvement of applications

Students showcase their LangChain and RAG projects

21 Wrap-Up and Future Directions

Discuss the future of LangChain and RAG in AI applications

Share resources for further learning and research in the field.

Earn Your Certification

After completing the course, you will receive a professional certificate from Code IT, verified by industry leaders in Nepal.

Share your achievement with pride on LinkedIn.

Course Mentors

Learn directly from industry experts with years of hands‑on experience

Er.Saurav Baral

Generative AI Instructor

Code IT, Nepal 4+ Years Experience

Anurag Sharma

Machine Learning Mentor

Cyber Alert Nepal 3+ Years

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Why Enquire?

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Flexible batch timings

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