Delhi, India

Generative AI

AI Application Development with Pythonin Delhi

40 Hours/5 daysLive virtual trainingIntermediate

Classroom training and venue arrangements depend on the availability of a suitable conference hall in Delhi, India. Dates, seating capacity, facilities and the final venue are confirmed before booking.

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Explore AI Application Development with Python

AI Application Development with Python training in Delhi, India provides practical skills to build intelligent applications using Python, APIs, AI models, automation, and modern development frameworks. Learn application architecture, Python programming, AI integration, data processing, API development, and deployment techniques. This course helps developers and AI professionals create scalable AI-powered applications, automate workflows, and develop real-world solutions for business and technology use cases.

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City-page course benefits

Build AI-Powered Applications – Learn to design and develop intelligent applications using Python, AI models, APIs, and modern development techniques in Delhi, India.
Strengthen Python Development Skills – Enhance Python programming skills for building scalable, maintainable, and AI-enabled software applications.
Learn AI Application Integration – Understand how to integrate AI capabilities, language models, and intelligent services into Python applications.
Develop API-Based AI Solutions – Gain practical knowledge of REST APIs, JSON, authentication, and service integration for connected AI applications.
Master AI Automation Techniques – Learn how Python can automate repetitive tasks, business workflows, data processing, and intelligent application operations.
Work with AI Development Frameworks – Explore relevant Python libraries and frameworks used for building modern AI-powered applications.
Improve Application Architecture Skills – Understand application structure, modular development, reusable components, error handling, and scalable design practices.
Develop Data Processing Skills – Learn techniques for handling, transforming, validating, and processing data required by AI applications.
Create Real-World AI Projects – Apply Python and AI development concepts through practical projects based on real-world business and technology scenarios.
Enhance AI Application Deployment Skills – Understand essential concepts for testing, packaging, deployment, monitoring, and maintaining Python-based AI applications.
Prepare for AI Developer Careers – Develop practical skills relevant to AI Application Developer, Python Developer, AI Engineer, and related technology roles.
Gain Career-Ready Python AI Expertise – Build hands-on expertise that helps professionals develop innovative AI solutions and pursue emerging opportunities in Delhi, India.

Course curriculum

Module 1: Introduction to AI Application Development

Overview of AI-powered application development
Evolution of AI applications and intelligent software
AI application development lifecycle
Key technologies used in Python-based AI applications

Module 2: Python Programming for AI Applications

Python syntax and programming fundamentals
Variables, data types, operators, and control statements
Functions, modules, packages, and exception handling
Object-oriented programming concepts
Python environments and dependency management

Module 3: Python Data Handling and Processing

Lists, tuples, dictionaries, sets, and data structures
File handling and data serialization
JSON and structured data processing
Data cleaning and transformation
Working with CSV and other common data formats

Module 4: AI and Generative AI Fundamentals

Introduction to Artificial Intelligence and Machine Learning
Generative AI concepts and applications
Large Language Models and foundation models
Tokens, context, inference, and model capabilities
AI application use cases and limitations

Module 5: Working with AI Models and APIs

AI model architecture and integration concepts
Connecting Python applications with AI APIs
API requests, responses, and authentication
JSON-based AI service integration
Managing API keys and application configuration

Module 6: Prompt Engineering for AI Applications

Fundamentals of effective prompting
Instruction, context, role, and output formatting
Zero-shot and few-shot prompting
Structured prompts for application workflows
Prompt testing and optimization

Module 7: Building AI-Powered Python Applications

AI application architecture and components
Designing Python applications with AI capabilities
Input processing and intelligent response generation
Managing application logic and AI workflows
Building practical AI application features

Module 8: REST APIs and Web Services

REST API fundamentals
HTTP methods and status codes
API authentication and authorization
Building API integrations with Python
Consuming external AI and business services

Module 9: AI Application Frameworks

Introduction to Python frameworks for AI applications
Application routing and request handling
Framework-based project organization
Integrating AI models with application frameworks
Building reusable application components

Module 10: Databases and AI Applications

Relational and NoSQL database concepts
Connecting Python applications with databases
CRUD operations and data management
Storing application and AI-generated information
Database integration best practices

Module 11: Retrieval-Augmented AI Applications

Fundamentals of Retrieval-Augmented Generation (RAG)
Document ingestion and text processing
Chunking and embedding concepts
Vector databases and similarity search
Connecting retrieved information with AI models

Module 12: AI Agents and Intelligent Workflows

Introduction to AI agents
Agent roles, goals, and actions
Tool-enabled AI applications
Multi-step AI workflows
Building intelligent task automation with Python

Module 13: AI Automation and Business Applications

Automating repetitive business processes
Python-based workflow automation
AI-assisted data processing
Intelligent document and content workflows
Developing AI solutions for real-world business requirements

Module 14: Testing and Debugging AI Applications

Python application testing fundamentals
Unit and integration testing
Debugging AI application workflows
Handling API and model-related errors
Validating AI application outputs

Module 15: Security and Responsible AI Development

Secure AI application development principles
API key and credential protection
Input validation and data security
Privacy and responsible AI practices
Managing AI risks and application vulnerabilities

Module 16: Application Deployment and Cloud Integration

Python application packaging
Deployment fundamentals
Environment variables and configuration management
Cloud-based AI application deployment concepts
Application monitoring and maintenance

Module 17: Performance Optimization and Production Readiness

Improving application performance
Managing API usage and resource consumption
Caching and efficient data processing
Logging and application monitoring
Designing reliable production-ready AI applications

Module 18: Hands-On AI Application Development Capstone

Define an AI application use case in Delhi, India
Design application architecture and technology components
Develop a Python-based AI application
Integrate AI models, APIs, databases, or external services
Implement testing, security, and deployment practices
Present and evaluate the completed AI application project

Exam details

Exam Level: Intermediate – AI Application Development with Python
Exam Type: Online assessment consisting of multiple-choice, scenario-based, and practical application development questions.
Number of Questions: 40 questions
Exam Duration: 90 minutes
Passing Score: 70% (28 out of 40)
Exam Topics: Python programming, AI fundamentals, Generative AI, LLM integration, prompt engineering, APIs, databases, RAG, AI agents, automation, testing, security, and deployment.
Assessment Mode: Online examination available to learners in Delhi, India.
Certification: StepMerit Learning Course Completion Certificate upon successful completion of the assessment.

Plan your learning

Who this course is for

Python Developers – Professionals looking to enhance their Python skills by building AI-powered applications and integrating modern AI technologies. Software Developers – Developers seeking practical expertise in combining software engineering with artificial intelligence and Generative AI capabilities. AI and Machine Learning Engineers – Professionals who want to develop production-oriented AI applications using Python, APIs, LLMs, and application frameworks. Generative AI Developers – Developers interested in creating practical applications using Large Language Models, prompt engineering, RAG, and AI services. Backend Developers – Professionals looking to build AI-enabled backend services…

Before you begin

Python Programming Fundamentals – Basic knowledge of Python syntax, variables, functions, loops, data structures, and object-oriented programming is recommended for AI Application Development with Python training in Delhi, India. Programming Fundamentals – Understanding basic programming concepts, problem-solving, debugging, and application development will help learners build AI-powered applications effectively. Artificial Intelligence Basics – Familiarity with fundamental AI concepts, machine learning, Generative AI, and Large Language Models (LLMs) is beneficial for understanding AI application development. API and Web Services Knowledge – Basic understanding of REST APIs, JSON…

Learning outcomes

Problem Statement – A transportation organisation manages large volumes of route information, vehicle data, schedules, passenger enquiries, service updates, maintenance records, and operational information. Transport teams may spend considerable time searching for information, analysing operational data, responding to passenger queries, and preparing reports. The organisation needs a practical AI application that can automate information processing, provide intelligent assistance, and improve operational efficiency using Python while maintaining accuracy, security, data privacy, and responsible AI practices. How We Overcame the Problem – Learners analysed transportation requirements and…

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Room facilities / classroom arrangements

General planning information. Availability is confirmed for your selected venue and batch.

Typical classroom arrangements — subject to confirmation for the selected batch: 1. Learning space and seating: A suitable room layout with seating and desk space for course discussions and exercises. 2. Presentation equipment: A display or projector for instructor-led explanations; equipment requirements are confirmed before delivery. 3. Internet and power: Connectivity and charging access where needed for the course’s digital resources or labs. 4. Collaboration tools: A whiteboard or flip chart for group activities, demonstrations and note-taking. 5. Room comfort: Appropriate lighting and ventilation, with breaks included in the agreed training schedule. 6. Access and learner needs: Accessibility, refreshments and any specific accommodation requirements should be discussed with the adviser in advance. This is general planning information. It does not confirm a particular venue, equipment package or included refreshments.

Course expertise

Professional & Training Experience: 5+ Years of professional and training experience in Python development, Artificial Intelligence, Machine Learning, Generative AI, LLM integration, and application development. Course Training Experience: 120+ Hours of training focused on Python programming, AI application development, API integration, LLM integration, prompt engineering, data processing, automation, and AI-powered application workflows. Candidates Trained: 180+ candidates, including Python developers, software developers, AI/ML professionals, IT professionals, data professionals, application developers, and technology learners. Training Delivery Experience: Experienced in online and…

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Insights for AI Application Development with Python

Learning insight 01

AI Application Development with Python: A Complete Guide to Building Intelligent Apps

Artificial Intelligence is transforming the way organizations build software, automate processes, analyze information, and deliver digital experiences. As AI capabilities become increasingly accessible through APIs and modern development frameworks, developers can use Python to create intelligent applications that…

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