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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

How can I enrol in a course?

Enrolling in a course is simple! Just browse through our website, select the course you're interested in, and click on the "Enrol Now" button. Follow the prompts to complete the enrolment process, and you'll gain immediate access to the course materials.

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

About the creator

UpSkill with Success Analytics

At our EdTech company, we are dedicated to transforming the way people learn by providing innovative and effective education solutions. We understand the importance of education in the lives of individuals and the society as a whole, and that's why we are committed to leveraging technology to make learning more accessible, engaging, and impactful.

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