Generative AI for Developer
UpSkill with Success Analytics
13 modules
English
Lifetime access
"Unleash the Power of AI: Learn to Create Cutting-Edge Applications and Innovative Solutions with Generative AI for Developers!"
Overview
Welcome to the Comprehensive Generative AI & Intelligent Systems Bootcamp, a hands-on, project-driven course designed to help you master the next frontier of Artificial Intelligence. From foundational concepts to real-world applications, this course walks you through the most powerful tools, techniques, and architectures shaping the future—like LLMs, RAG, Agentic AI, and multi-modal systems.
Through 4 real-world MVP projects, you'll not only learn theory but build fully functional AI-powered applications using technologies like LangChain, FAISS, Tesseract, Streamlit, Twilio, FastAPI, and more. Whether you're a data scientist, engineer, developer, or tech enthusiast, this course will equip you with job-ready skills in the most exciting domain of AI today.
Key Highlights
4 Real-world Gen AI & Agents AI MVP Products
50+ hours of content with hands-on coding
Multilingual + Multi-modal AI implementation
Build deployable apps using Streamlit, FastAPI, and React
Translate AI knowledge into job-ready experience
Perfect for developers, analysts, product engineers, and AI enthusiasts
What you will learn
Professional & Descriptive
By the end of this course, you'll be able to build intelligent, real-time, and scalable GenAI-powered systems — from document Q&A to voice-based AI agents — all using modern tools and architectures.
Modules
Course Features
5 attachments
Lifetime Dashboard Access
Live Doubt Clearing Sessions
Resume Discussion And Mock Interview
Paid Internship Opportunity In Research Project for Freshers
Job Referrals for Data Science & GEN AI positions ( Expect 40-70% hike on current Salary )
Introduction to Generative AI
4 attachments
What is Generative AI?
History and evolution of AI and Generative AI
Use cases across industries
Ethical considerations and responsible AI usage
Large Language Models (LLMs)
6 attachments
What are LLMs?
Transfermor Architecture and how they work
Popular LLMs: GPT, BERT, Claude, LLaMA
Training and fine-tuning LLMs
Understanding ablout Langchain & its Implementation
Understanding embeddings and vector representations
System Prompt Research and Engineering
8 attachments
Introduction to prompt engineering
Types of prompts
Instruction-based prompts
Chain-of-thought prompts
Few-shot and zero-shot prompts
Advanced techniques for designing effective prompts
Evaluating prompt performance and bias
Prompt optimization tools and techniques
Vector DataBase
13 attachments
What are vector databases?
Role in modern AI applications
Why traditional databases fall short for similarity search
Differences in data storage and retrieval
Use cases for each
Indexing and search efficiency
High-dimensional vector space
Distance metrics: cosine similarity, dot product, Euclidean
Approximate Nearest Neighbor (ANN) search algorithms
Overview & Complete Implementation: FAISS, Pinecone, Weaviate, Qdrant, ChromaDB
Pros and cons of each Vector DB
Generating embeddings using OpenAI, HuggingFace, Cohere, etc.
Best practices for storing and versioning embeddings
Retrieval-Augmented Generation (RAG)
13 attachments
Introduction to RAGs
RAG vs traditional LLM generation
How RAG works
Two-phase architecture: Retrieval + Generation
Overview of Dense Passage Retrieval (DPR)
Role of vector databases (e.g., FAISS, Weaviate, Pinecone)
Architecture of a RAG pipeline
Handling structured vs unstructured data
Use of embedding models (e.g., OpenAI, HuggingFace, SentenceTransformers)
Integration of LLMs with external data using RAG
Building a RAG-based system
Connecting LLMs to knowledge bases for real-time information generation
Improving accuracy and factuality of AI-generated content
Agents and Agentic AI
9 attachments
Introduction to AI agents
Single-agent vs. multi-agent systems
Understanding Agentic AI
Characteristics of Agentic AI
Real-world applications (e.g., customer support, autonomous systems)
Building intelligent agents using LLMs
Integrating with APIs
Context retention and action-based responses
Multi-modal AI agents (text, image, video inputs)
Building a Multilingual RAG-Based Document Q&A - MVP 1
10 attachments
Build a system where users upload PDF, DOCX, or image files
Extract content and allow users to ask questions in English, Hindi, or Marathi
Provide accurate, context-aware answers using a RAG architecture
Ensure OCR + QA performance on noisy scans
Create test cases with documents in all three languages
Understand how to apply RAG architecture to real-world document Q&A
Learn to build multilingual applications using translation + language detection
Gain hands-on experience with file parsing, OCR, embedding models, and vector search
Integrate LLMs with external tools and pipelines for domain-specific Q&A
Teck Stack Used: Python, OCR Tesseract, Google Model, Langchain, VectorDB, transformers, Streamlit, FastAPI, React for UI Development etc
Call Center Analysis - Multi-Speaker Audio Platform - MVP 2
17 attachments
Understand the core goals: transcribing spoken content, identifying multiple speakers, and summarizing long conversations.
Explore real-world applications such as podcasts, interviews, meetings, and customer support analytics.
Learn how to extract audio from YouTube videos and local video files
Measure and validate audio duration before processing.
Use and Develop open-source transcription model on local machines.
Compare speed, accuracy, and latency between APIs.
Building Speaker Diarization Module
Detect and differentiate between multiple speakers using WhisperX’s diarization module.
Assign speaker labels (e.g., Speaker 1, Speaker 2) to each word or sentence.
Align words and segments accurately with timecodes.
Understand benefits of character-level alignment for subtitle creation or searchability.
Structure output for easy integration into UIs, dashboards
Handle errors like missing audio, failed transcription, or model timeouts.
Track processing time and log detailed outputs for auditing.
Improve performance by caching models and batching requests.
Design ideas for deploying a Streamlit or React-based UI.
Learn how to pitch this project for freelance work, startups, or data science portfolios.
AI-Powered Personalized Agent ( NewsAgent AI ) - MVP 3
11 attachments
Learn how to integrate APIs from multiple content platforms ( Youtube, Medium, News Channel, Instagram )
Understanding about project Architecture
Multi-Platform Content Aggregation
Retrieves latest articles from specified authors
Understand AI-based summarization using LLMs
Content Processing & Generates concise and meaningful summaries
Maintains context and key information from original content
Digest Generation
Email Delivery System & Sends formatted markdown digests via email
Build a full-stack content pipeline (Fetch → Summarize → Format → Send)
Automate user-centric email delivery using AI
Voice AI Agent (MVP 4)
8 attachments
Use Twilio or n8n + Twilio to handle incoming/outgoing voice calls
Use n8n as the central no-code automation platform to orchestrate workflows
Integrate Twilio (via n8n) to handle incoming and outgoing voice calls
Connect with Retell AI to power real-time conversation and generate human-like voice responses
Design conversational flow visually inside n8n using trigger, condition, and HTTP request nodes
Automate follow-up actions like sending SMS, saving to Google Sheets, or emailing call summaries
No coding required — fully drag-and-drop based configuration with reusable templates
Scalable for small businesses, support centers, or custom voice-based bots
AWS Deployment Integration
1 attachment
AWS Deployment Integration
Important Announcement
1 attachment
Join our Live 3-Day Generative AI Hackathon and stand a chance to win exciting prizes, including ₹50,000 cash and an iPad worth ₹45,000. Don’t miss this opportunity to showcase your skills and compete with top talent in real-time!
FAQs
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Can I access the course materials on any device?
Yes, our platform is designed to be accessible on various devices, including computers, laptops, tablets, and smartphones. You can access the course materials anytime, anywhere, as long as you have an internet connection.
How can I access the course materials?
Once you enrol in a course, you will gain access to a dedicated online learning platform. All course materials, including video lessons, lecture notes, and supplementary resources, can be accessed conveniently through the platform at any time.
Can I interact with the instructor during the course?
Absolutely! we are committed to providing an engaging and interactive learning experience. You will have opportunities to interact with them through our community. Take full advantage to enhance your understanding and gain insights directly from the expert.
About the creator
UpSkill with Success Analytics
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