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Generative AI
Generative AI Course – Master AI Models for Content Creation & Automation
Course Overview
Join Nepal’s Most Affordable Python with Machine Learning, Deep Learning & Generative AI Training
Code IT offers Nepal’s most affordable Python with Machine Learning and Generative AI training course, ideal for students, IT professionals, and job seekers from Dharan, Itahari, Biratnagar, Koshi, Mechi, Kathmandu, and all across Nepal. This Python and AI course in Nepal provides in-demand skills in machine learning, deep learning, and generative AI — combining real-world projects, practical coding, and expert mentorship. Whether you're looking to build a career in AI, data science, or automation, this is the best AI training course with certification and internship in Nepal.
What You Will Learn:
- Python programming for AI and machine learning
- Data analysis, visualization, and model training techniques
- Supervised, unsupervised, and deep learning algorithms
- Hands-on projects using real datasets
- Tools like ChatGPT, DALL·E, and other Generative AI platforms
- AI-powered application development using Python
- Responsible AI, ethics, and industry applications
Why Code IT?
- Code IT offers Nepal’s best affordable AI course with real project experience
- Certification and guaranteed internship
- Online & offline classes available across Nepal
- Long-term career support and mentorship
Start your journey with Code IT’s top-rated Python with Machine Learning and Generative AI course in Nepal — and master the future of technology with hands-on experience.
What's Included in the Course
Pre-Recorded Video
Perfect if you're outside Nepal or can't attend live sessions.
- Lifetime access to recordings
Course Syllabus
Explore the complete course syllabus to see what you'll learn from start to finish.
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.)
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.
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
Deep Learning Frameworks
- Overview of TensorFlow and PyTorch
- Hands-on: Set up TensorFlow or PyTorch
- -- Create a basic neural network.
Training and Validation
- Overfitting, underfitting, and regularization techniques
- Hyperparameter tuning
- Hands-on: Train a model on the MNIST dataset.
Introduction to Generative Models
- What are generative models?
- -- Autoencoders, Variational Autoencoders (VAEs), GANs, Transformers
- -- Discriminative vs Generative models
- Hands-on: Build a simple autoencoder.
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.
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.
Transformer Models
- Key concepts: Self-attention, encoder-decoder architecture
- Hands-on: Build a mini-transformer for text generation.
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.
Ethical Considerations in Text Generation
- Biases in generative models
- Case studies on ethical issues and mitigation strategies.
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).
Advanced GANs
- Conditional GANs (cGANs) and StyleGANs
- Hands-on: Modify image styles using StyleGAN.
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.
Discussion and Review
- Compare GANs, Diffusion models, and Transformers
- Real-world applications for each.
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.
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
Capstone Project with LangChain and RAG
- Students integrate LangChain and RAG into their capstone projects
- -- Example: Build a domain-specific chatbot or knowledge assistant.
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.
Feedback, Iteration, and Final Presentations
- Gathering and incorporating feedback into projects
- Iterative improvement of applications
- Students showcase their LangChain and RAG projects
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.
Need More Information About This Course?
Have questions or need clarification? Our education specialists are ready to assist you. Complete the form below and we'll respond within 1 hours.
Frequently Asked Questions
Code IT is a professional IT training institute that offers both online and offline courses in various fields like Web Development, Networking, Graphic Design, and more.
Yes, you will receive a certificate upon successful completion of the course.
Internship opportunities are available for most students; however, some courses do not include internships.
Yes, we offer job placement support. Terms and conditions apply.
Yes, the course fee must be paid during registration to confirm your seat.
Yes, demo classes are available. You can find them at the top of this syllabus — click the "Watch Demo" button.
Yes, you will get access to recorded class videos, which you can watch anytime with lifetime access.
Yes, Code IT provides lifetime support to all students, even after course completion.
No, the fee is non-refundable. However, you can transfer to another class if you inform the administrator within 1 day of the course start date.
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