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Advanced Certificate Program in Generative AI

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Advanced Certificate Program in Generative AI

This program covers the foundational concepts of AI and machine learning, focusing on advanced generative models, such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). Participants will learn how to apply these models to real-world scenarios, enhancing their ability to innovate and solve complex problems in various industries.

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Requirements

Basic Knowledge of Programming, Understanding of Machine Learning Fundamentals, Mathematics Proficiency, Experience with Data Science Tools, Analytical and Problem-Solving Skills, Interest in Artificial Intelligence

Description

Advanced Certificate Program in Generative AI
Overview

Leverage advanced GenAI to boost innovation and drive strategic decisions.

Duration	5 months
Total Fee	₹1,01,000
Mode of learning	Online
Difficulty level	Beginner
Official Website	Go to Our Course
Credential	Certificate

Advanced Certificate Program in Generative AI

Highlights

Discover More

Generative AI Models
Transformers

Prompt Engineering
Product Development
Deploy Web Apps with Flask

Advanced Certificate Program in Generative AI
Course Details

Skills you will learn

Generative AI Models, Transformers, ChatGPT, Prompt Engineering Product Development, Deploy Web Apps with Flask

More about this course

Learn 10+ Generative AI tools, including a ChatGPT course
Up-to-date Generative AI modules
Upskill through real projects
Gen AI masterclasses by industry experts.
upGrad Alumni Status.

Advanced Certificate Program in Generative AI
Curriculum

Course 1: Programming 101

✓ Introduction to Python and Programming
✓ Python Data Types, Variables, Operators, Data Structures
✓ Python Programming Constructs: Conditionals, Loops, Functions
✓ Python for Data Science and Pandas: Working with relational databases, Data Cleaning, Preprocessing, Analysis
✓ Advanced Text Processing using Pandas
✓ Basics of Linux: Commands, Setting up Local Environment

Course 2: Create ShopAssistAI

✓ Define the different components of the bot and design the workflow for creating the bot
✓ Understand the working of LLMs like GPT3 that power ChatGPT: Attention Mechanisms, Transformers, Reinforcement Learning, RLHF among others
✓ Apply prompting techniques to create prompts for asking questions and evaluating the customer's response
✓ Establish metric(s) to measure model performance
✓ Prompt Engineering: Improve the assistant's responses by applying simple (non-reasoning) prompting techniques
✓ Prompt Engineering: Improve the assistant's accuracy by applying Chain of Thought reasoning-based prompting techniques
✓ Apply fine-tuning using OpenAI APIs to train an LLM on your custom data
✓ Learn the best practices for fine tuning OpenAI APIs
✓ Transfer learning: Apply the same principles to other problems in your domain
✓ Deploy and launch ShopAssistAI application on Flask
✓ Iterate and improve the UI of the app using ChatGPT's code writing capabilities

Course 3: Create Mr.HelpMate AI

✓ Understand various search techniques and the generative search paradigm
✓ Understand the working of embeddings and how they help in semantic search
✓ Create and analyse embeddings for semantic search
✓ Understand the entire semantic search pipeline including chunking, embedding, and retrieval
✓ Create embeddings for large documents by creating chunks
✓ Create a Q/A system that fetches answer using similarilty search over embeddings
✓ Scale the Q/A system by making use of vectorstores like ChromaDB
✓ Embed, index large documents and search in Vectorstore
✓ Integrate LLM chat models like GPT with the semantic search to build a retrieval augmented generation system that directly responds to user queries
✓ Experiment with different vectorstores, search and index algorithms and LLMs to improve the chatbot

Course 4: SemanticSpotter

✓ Define the components of the knowledge retrieval system and design the workflow
✓ Explore how LangChain can connect the different components of the system
✓ Understand the different parts of LangChain - Models, Prompts, Indexes, Chains, Memory and Agents
✓ Explore the different tools in LangChain and initialise an agent that uses the tools to read different types of files or data present in the company database
✓ Build the backend for the system using Vectorstore options present in LangChain
✓ Divide the documents into chunks and apply the LLM to create the embeddings and extract entity for the chunks of document and store them in the Vectorstore
✓ Construct the Search Index and Entity Store and create a functionality to update it with every question that the user asks
✓ Use the Chain functionality of LangChain to connect all the components
✓ Evaluate the results and improve them by experimenting with different LLMs, indexing and embedding algorithms
✓ Explore other agents and tools to improve the system like adding features like automatic email notifications on some issues, etc.

Course 5: Future Developments in Generative AI

✓ Mitigating risks in AI: Responsible AI
✓ RLHF as a Product to train your own LLM
✓ Multimodal Learning: Audio, Image, Text, Heatmap among others within a LLM

Course 6: Future Developments in Generative AI

✓ Mitigating risks in AI: Responsible AI
✓ RLHF as a Product to train your own LLM
✓ Multimodal Learning: Audio, Image, Text, Heatmap among others within a LLM

Course 7: Create PixxelCraft AI

✓ Understand how images are stored and manipulated digitally and work on image processing tasks
✓ Understand the process by which artificial neural networks and their variants such as convolutional neural networks handle image analysis
✓ Understand and implement legacy image generation models such as variational autoencoders and generative adversarial networks
✓ Understand the components of diffusion models and the process by which images are generated and work on building a stable diffusion pipeline component-by-component

Course 8: Create ShrewdNews AI

✓ Understand prompting for code generation and generate code for data science tasks in a larger ML problem
✓ Automate ML workflows using language generation models including data preprocessing and machine learning modelling
✓ Use vector embeddings to solve a real-world use-case problem based on semantic similarity
✓ Fine-tune language generation models for a particular problem statement and evaluate the model

Advanced Certificate Program in Generative AI
Entry Requirements

Eligibility Criteria

Bachelor's Degree (4 years program) or a Masters Degree in the relevant discipline with at least 55% marks. Minimum of 2 years of work experience

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