Build your AI skills from Python fundamentals to advanced AI applications with FLM’s AI Mastery Course. Learn AI foundations, LLMs , prompt & context engineering, API integration, FastAPI, workflow automation, RAG, Agentic AI, Lang Graph, MCP, AI evaluation, and deployment through practical assignments and hands-on projects.
Training in Telugu
Recordings
Addon Sessions
Projects
AI Mastery is the practical skill of using AI to work smarter, automate tasks, create better solutions, and build intelligent workflows using modern AI tools and techniques.
Explore agents, tool calling, state management, LangGraph, MCP, A2A concepts, n8n workflows, and controlled actions with human approval.
Understand AI, machine learning, deep learning, large language models, tokens, embeddings, context windows, and model limitations.
Learn how to connect AI models to documents and data sources to create evidence-based answers, knowledge assistants, and practical business applications.
We take you from beginner level to building practical, production-ready AI systems using the most in-demand tools and frameworks in today’s AI ecosystem.
Begin with Python, Git/GitHub, AI foundations, and LLM concepts.
Work with SQL, REST APIs, FastAPI, Pydantic, and backend development.
Study prompt engineering, multimodal AI, RAG, and knowledge assistants.
Learn Lang Graph, tool integration, MCP, and workflow automation.
Explore testing, hallucination evaluation, prompt injection, privacy, governance, and responsible AI.
Build knowledge assistants and AI agents using LangChain integrations, tool calling, state management, conditional workflows, and human approval.
Go from AI fundamentals to building practical AI applications using modern tools, frameworks, automation platforms, and agentic AI technologies.
Build Python applications using variables, functions, files, packages, error handling, and API integrations. Work with REST APIs, JSON, Postman, and Swagger UI.
Learn version control, repositories, commits, branches, pull requests, dependency management, and secure configuration using Git and GitHub.
Explore AI foundations, Large Language Models, prompt and context engineering, structured outputs, hallucinations, AI safety, and prompt evaluation.
Learn document processing, embeddings, semantic search, vector databases, retrieval pipelines, and source-grounded answers using FAISS or Chroma.
Build AI chains, tool-enabled agents, memory-based workflows, and multi-step agentic systems using LangChain and LangGraph.
Create automated workflows using triggers, webhooks, API integrations, AI model calls, approval routing, error handling, and execution logs.
Python Fundamentals & Programming Logic
Variables, Data Types & Operators
Conditional Statements & Loops
Data Structures & Functions
File Handling, CSV & JSON
Error Handling & Debugging
Classes, Objects & OOP
Modules, Packages & Virtual Environments
API Response Processing
Git & GitHub Fundamentals
Repository Management
Commits, Branches & Pull Requests
Push, Pull & Clone
README & .gitignore
Dependency Management
Environment Variables & API Key Protection
AI Development Workflow
Artificial Intelligence Fundamentals
Machine Learning & Deep Learning
NLP & Computer Vision
Supervised & Unsupervised Learning
Transformers & Foundation Models
LLMs, Tokens & Embeddings
Generative, Reasoning & Multimodal Models
Model Selection & Hallucinations
Decision Models & Classification
Fine-Tuning, LoRA & PEFT
Prompt Engineering Fundamentals
Zero-Shot & Few-Shot Prompting
Prompt Templates & Optimization
Structured Outputs & JSON
Context Engineering & Memory
Source Grounding
Agent Skills & SKILL.md
Hallucinations, Bias & Privacy
Prompt Injection & Guardrails
Prompt Evaluation
Text, Image, Audio & Video AI
Document Intelligence & OCR
Data Extraction & Validation
Enterprise Data Cleaning & Analytics
AI-Powered Reporting & Decision Support
AI Applications in QA, HR & Operations
Marketing & Business Applications
Human Verification & Responsible AI
Knowledge Assistants & Business Agents
Relational Database Fundamentals
Tables, Records & Relationships
SQLite & PostgreSQL
SQL Queries & Filtering
Sorting, Grouping & Aggregations
Joins, Update & Delete
Parameterized Queries
Data Validation & Database Permissions
Python-to-SQLite Integration
Client-Server Architecture
HTTP, HTTPS & REST APIs
JSON & API Endpoints
GET, POST, PUT, PATCH & DELETE
Headers, Parameters & Request Bodies
Status Codes & Error Handling
Authentication & Authorization
Bearer Tokens & Rate Limits
Postman API Testing
Swagger UI & OpenAPI
FastAPI Fundamentals
Uvicorn & API Routing
REST Endpoints & CRUD Operations
Path, Query & Body Parameters
Pydantic Models & Validation
SQLite Database Integration
Error Handling & Environment Configuration
API Testing with pytest
TestClient & HTTPX
Health Checks & Streaming/SSE
LLM API Integration
Model SDKs & Authentication
Structured Prompting & JSON Validation
Pydantic Output Validation
Token Usage, Latency & Cost
Model Parameters & Rate Limits
Retries, Timeouts & Streaming
Tool Calling
Decision Models, Scoring & Thresholding
LLM Integration with FastAPI
Automation vs AI Agents
Triggers, Nodes & Branching
Webhooks & Scheduling
HTTP Requests & Data Transformation
External API Integration
AI Model Integration
FastAPI Integration
Document & Customer Workflows
Human Approval & Routing
Error Handling, Retries & Execution Logs
MCP Integration Awareness
RAG Fundamentals
RAG vs Fine-Tuning
Document Ingestion & Parsing
Chunking & Metadata
Embeddings & Semantic Search
FAISS & Chroma Vector Databases
LangChain Document Loaders
Text Splitters & Retrievers
Grounded Answers & Citations
RAG Evaluation & Access Permissions
Agentic AI Fundamentals
AI Agents vs Workflows
Planning & Task Decomposition
Tool & Function Calling
LangGraph Nodes & Edges
Conditional Routing
State, Memory & Checkpointing
Human-in-the-Loop Approval
Multi-Agent Delegation
Supervisor Patterns & Failure Recovery
Model Context Protocol (MCP)
MCP Hosts, Clients & Servers
Tools, Resources & Prompts
JSON-RPC & Transport
MCP Server Development
MCP Permissions & Security
Agent-to-Agent (A2A) Protocol
Agent Cards, Messages & Tasks
Agent Delegation & Interoperability
Agent Skills & Protocols
Golden Datasets
AI Evaluation Methods
LLM-as-a-Judge & Human Evaluation
Relevance, Factuality & Faithfulness
RAG Evaluation & Retrieval Metrics
Agent Testing & Regression Testing
RAGAS & Promptfoo
Automated Testing with pytest
Prompt Injection & Tool Security
Privacy, Fairness & AI Governance
Development, Staging & Production
Application Packaging
Uvicorn & Environment Configuration
Docker Images & Containers
Deployment Options & API Health Checks
Rollback & Application Logs
Latency & Token Monitoring
OpenTelemetry & Langfuse
Timeouts, Retries & Rate Limits
Incident Investigation & Runbooks
GitHub Actions & CI/CD Concepts
Problem Discovery & User Research
Product Requirements Document (PRD)
User Stories & Acceptance Criteria
MVP Scope & UI/UX Flows
AI-Assisted Coding
Specification-Driven Development
Frontend & Backend Integration
Security, Testing & Deployment
Documentation & Product Demonstration
GitHub Portfolio & Career Readiness
Final Capstone Project
Build an internal knowledge assistant using company policies, SOPs, onboarding manuals service-guide PDFs. The assistant answers questions using document evidence and handles unsupported questions safely.
Python, LangChain, FAISS or Chroma, FastAPI, Pydantic, pytest, RAGAS or Promptfoo, Langfuse, Docker, GitHub, SQLite (optional).
AI workflow for incident investigation , resolution recommendations using a simulated service-health API, SQLite ticket system, SOP knowledge base & n8n notifications. Require human approval before protected changes.
Python, LangGraph, LangChain, FastAPI, SQLite, n8n, MCP Python SDK, pytest, Promptfoo or RAGAS, Langfuse, Docker, GitHub.
Train and evaluate an image classifier to distinguish acceptable packaging from visibly damaged packaging. Measure reliability, identify false positives and false negatives, and determine when human review is needed.
Google Teachable Machine, Label Studio Community (optional), Google Sheets or equivalent scorecard, MLflow (instructor-guided demonstration).
Analyze synthetic customer feedback covering shipping, quality, billing and usability. Categorize sentiment, issues and urgency; compare prompt versions; and generate business insights with human verification.
KNIME Analytics Platform, KNIME AI Extension (when instructor-configured), Langfuse, Promptfoo (guided evaluation), spreadsheet or dashboard.
Application Creation and Update Sets
Build a custom ServiceNow application, configure forms and layouts, and migrate changes across instances using Update Sets.
🛠 Tools Used: ServiceNow Studio • Forms • Update Sets • Tables
Onboarding Service Catalog
Create an employee onboarding catalog with service requests for laptops, mice, and other organizational resources
🛠 Tools Used: Incident Management • Forms • UI Policies • Business Rules
Incident Management
Configure the Incident Management lifecycle with forms, field conditions, and workflow customization
🛠 Tools Used: Incident Management • Forms • UI Policies • Business Rules
Change Management
Implement the Change Management lifecycle with customized forms and approval workflows.
🛠 Tools Used: Change Management • Forms • Workflows • UI Policies
Problem Management
Build the Problem Management lifecycle with field configurations and process customizations.
🛠 Tools Used: Problem Management • Forms • Business Rules • UI Policies
Transform Map
Import bulk Excel data into ServiceNow using Transform Maps while preventing duplicate records.
🛠 Tools Used: Import Sets • Transform Maps • Excel • Data Load
👉 Module Wise Quizzes, Assignments, and Practice Questions
👉 100% Placement assistance
👉 Industry professionals with 10+ years of experience
👉 Live Chat support assistance
👉 Access to session recordings
👉 EMI Option also available with specified cards
👉 Get ₹10,000 Worth of Free Career Booster Add-On Sessions
👉 Dedicated Job Updates
👉 100% Live Training in Telugu
👉 Industry Ready Training
👉 Interview Questions & Study Materials
👉 Resume & Linked In Preparation
Every program includes industry-focused add-on sessions designed to help students crack jobs, freelance, switch careers, and grow professionally.
Everything you need beyond coding to become job-ready.
Session 01
Session 02
Session 03
Session 04
Session 05
Session 06
Session 07
Session 08
Session 09
Session 10
Session 11
Rs. 34,999/-
Rs. 13,999/-
Rs. 21,000/-
AI-related careers offer some of the most aggressive salary growth in the modern economy. Here is what the market is paying:
Job Role
Experience
Average Salary
Prompt Engineer
Fresher
₹5.0 – ₹10.0 LPA
AI Automation Specialist
Fresher
₹5.0 – ₹12.0 LPA
Generative AI Developer
Fresher
₹8.0 – ₹15.0 LPA
AI Workflow Consultant
Fresher
₹6.0 – ₹12.0 LPA
Freelance AI Architect
Fresher
Project-Based
Job Role
Experience
Average Salary
Prompt Engineer
2–5 Years
₹18.0 – ₹35.0 LPA
AI Automation Specialist
3–6 Years
₹20.0 – ₹40.0 LPA
Generative AI Developer
4–8 Years
₹25.0 – ₹60.0 LPA
Salesforce Developer
3–5 Years
₹20.0 – ₹35.0 LPA
Freelance AI Architect
5+Years
Project – Based
At Frontlines Edutech, we believe in transforming careers through high-quality, industry-relevant education. Our focus is on providing practical skills and knowledge that lead to real-world success. With a proven track record of impactful learning, we ensure our students are well-equipped to excel in their chosen fields and achieve their career goals with confidence.
A: No! This course focuses on Applied Generative AI and Prompt Engineering. You do not need to know complex Machine Learning algorithms or heavy Python coding. We teach you how to use logic, frameworks, and “Low-Code” tools to command AI models to do the heavy lifting for you.
A: Yes. The syllabus specifically states that special care is taken for non-IT students.
A: The syllabus identifies students, freshers and working professionals as suitable learners.
A: Yes! All sessions, practical tool implementations, and doubt-clearing classes are taught in Telugu. We break down complex concepts like RAG systems, Vector Databases, and LangChain in your regional language to ensure absolute conceptual clarity.
A: YouTube shows you basic chat tricks. We train you on Enterprise AI architecture. You will learn to build RAG (Retrieval-Augmented Generation) systems, deploy autonomous AI Agents, and use professional frameworks like LangChain and LangGraph to build secure, production-ready AI applications for real businesses.
A: While standard ChatGPT just answers questions, AI Agents can actually take action. They can browse the web, send emails, trigger workflows, and make logical decisions on their own. Mastering AI Agents puts you at the absolute cutting edge of the industry.
A: This course is completely project-driven. You will build and deploy real-time assets—including Custom AI Chatbots, RAG Document Assistants, and Automated Workflows—to showcase directly to hiring managers on your GitHub and LinkedIn.
A: Because AI is driving massive revenue and efficiency for businesses, AI professionals are paid a premium. Freshers can expect starting salaries between ₹5 LPA and ₹12 LPA. With experience in building complex automation pipelines, salaries rapidly scale to ₹20 LPA to ₹40 LPA+.
A: It is currently the most lucrative freelance skill in the world. Every business wants to implement AI but doesn’t know how. You can easily take on high-paying freelance projects, setting up AI customer support bots or content automation pipelines for international clients.
A: Yes. We provide comprehensive placement support. This includes aggressive portfolio reviews, building an ATS-friendly AI professional resume, conducting live technical mock interviews, and providing direct job updates for top tech companies.
A: Yes. Upon completing the training and deploying your real-time AI projects, you will receive an industry-recognized certificate from Frontlines EduTech to massively boost your LinkedIn profile and resume credibility.
A: No. The course syllabus states that no prior programming or AI knowledge is required.
Yes, you’ll get a Certificate representing your Industry Readiness once you submit your projects and clear the pre placement test.
