Deep Learning, Generative Course in Nepal

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

Generative AI

Looking for the best Generative AI course in Nepal?
Join the professional Generative AI Training at Code IT and learn how to create AI-powered content, models, and applications using the latest generative AI technologies and tools.

This hands-on AI course is ideal for students across Nepal who want to master AI, gain practical experience, and develop job-ready skills in artificial intelligence. You’ll work on building AI models, generating text, images, and other media, and applying AI solutions for real-world projects.

Our training program offers live online classes accessible from anywhere in Nepal, along with hands-on classroom sessions in Dharan. Develop practical AI skills, work on cutting-edge projects, and prepare for professional opportunities in artificial intelligence, machine learning, and data-driven innovation.

Master generative AI techniques, build real-world projects, and gain the confidence to work as a professional AI developer in Nepal’s technology-driven industry.

Prerequisites

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

Generative AI

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Mode: Online (Google Meet) Google Meet
Duration: 1 month
Rs.2,499/-
Rs.30,000 Save 91%
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WhatsApp: 9862130505
Telephone: 025-575163

Everything You Receive

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

Live Classes

Google Meet
7:00 PM - 8:00 PM

Lifetime Videos

Re-watch anytime

Certification

Industry recognized

Internship

Internship is not currently available.

Course Curriculum

Everything you'll learn — from fundamentals to advanced concepts

What you will learn

  • Generative AI Fundamentals

  • Text Generation & Language Models

  • Image Generation & Creative AI

  • Projects & Real-World Applications

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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.
Certificate

Course Mentors

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

Er.Saurav Baral

Er.Saurav Baral

Generative AI Instructor

Code IT, Nepal 4+ Years Experience
Anurag Sharma

Anurag Sharma

Machine Learning Mentor

Cyber Alert Nepal 3+ Years
Get in Touch

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