


Generative AI
Course Description
Generative AI plays a pivotal role in revolution to IT sectors. It can enhanced the personalized learning through mastering in machine learning, artificial intelligence etc. At CodeIT, it offers a practical and real relevant approach to understand and using generative AI. It emphasizes hands on learning, creating a diverse idea. Learners will be able to create or build real-world applications, including AI chatbots, artistic image synthesis tools, voice generation systems. At CodeIT we believe that financial barrier should not hinder the study of a students, the most in demand course could bring the alternative solutions to your websites as CodeIT is offering it at very affordable price.
Why choose Code IT?
Benefits
It is designed for both beginners and a experienced developers who wants to enhance knowledge, along with hands on experience and real time projects.
- Hands on experience: youll gain the real practical knowledge, and create your own personal portfolio websites and githubs
- Affordable: codeit offers its training in most affordable prices where student can learn without hampering with their financial barriers
- Certification and life time support: after completion of the course youll be guided to make the real time projects that might be accepted by real business as well as youll get the free certificates along with the recorded videos of your online classes for the life time.
- Ethical AI awareness: the course integrates training on responsible AI development, teaching learners how to mitigate bias, ensure transparency, and build ethical AI systems.
Who can learn this?
CodeIT Generative AI course is designed to be assessable to different learners from beginners to the advanced. It's suitable for anyone looking to enhance their knowledge,
- Beginners: this course is design to give prior to the beginers who are new comers and have knowledge about ai and machine learning
- Data scientist and engineer: who want to aced the professional learning in data science and ai or machine learning
- Tech Enthusiasts: Anyone passionate about data science, machine learning, or AI who wants to explore new skills and projects will find our courses engaging and fulfilling.
Materials included
Requirements
Course Syllabus
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.