This beginner-to-intermediate bootcamp develops practical skills in building AI-enabled applications and integrating AI capabilities into software systems. Participants progress from Python programming and machine learning fundamentals to pretrained model inference, computer vision, reusable AI components, REST APIs, and web application integration.

The course covers Python, NumPy, Pandas, OpenCV, PyTorch, pretrained YOLO-family models, FastAPI, and JavaScript integration. Applications include text analysis, image classification, object detection, AI-assisted inspection, and sensor-data analysis. A final capstone brings these skills together in an end-to-end AI application prototype.

The course focuses on application development and proof-of-concept integration. Production readiness depends on the selected use case, data quality, computing resources, and further validation.

 

Duration 20 Days – 140 hrs.

 

Objectives

• Explain the fundamentals of artificial intelligence, machine learning, and deep learning.
• Develop modular Python applications for AI-enabled solutions.
• Prepare and process structured data, text, images, and video inputs.
• Apply pretrained models for classification, prediction, and object detection.
• Interpret model outputs, confidence scores, evaluation metrics, and limitations.
• Build computer vision applications using OpenCV and pretrained object detection models.
• Develop reusable inference components and integrate predictions into application workflows.
• Create REST APIs using FastAPI and structured request and response models.
• Connect AI services to web interfaces and existing software components.
• Apply input validation, error handling, logging, and basic performance optimization.
• Explain deployment requirements and introductory real-time integration concepts.
• Build, test, document, and demonstrate an end-to-end AI application prototype.

 

Target Audience 

• Software developers seeking to incorporate AI capabilities into applications.
• Programmers transitioning into AI application development.
• Backend and web developers interested in AI services and REST API integration.
• Technical professionals developing computer vision and inspection prototypes.
• IT and system integration professionals working with AI-enabled workflows.
• Engineering and data professionals interested in practical AI application development.

 

Prerequisites 

• Basic programming knowledge, including variables, conditions, loops, and functions.
• Familiarity with web application concepts and client-server communication.
• Basic understanding of data files and common formats such as CSV and JSON.
• A development computer capable of running Python and the required libraries, with permission to install software.
• Access to suitable sample data and pretrained models for the selected applications.
• Camera hardware if live camera integration is included.
• No prior machine learning or deep learning experience is required.

 

 

Course Outline 

Phase 1: Python Programming Fundamentals for AI Development (3 Days)

Day 1: Python Programming Essentials 

• Python installation and development environment setup
• Variables, data types, operators, and type conversion
• Conditional statements and loops
• Functions, parameters, return values, and scope
• Hands-on programming exercises

Day 2: Python Data Structures and Application Development 

• Lists, tuples, dictionaries, and sets
• Strings, comprehensions, and common built-in functions
• Modules, packages, and exception handling
• File handling and JSON processing
• Object-oriented programming fundamentals

Day 3: Python Libraries for AI Development 

• Virtual environments and package management
• NumPy arrays, indexing, slicing, and basic numerical operations
• Introduction to Pandas for data handling
• Installing and configuring OpenCV and PyTorch
• Reading images and videos using Python
• Introduction to AI development workflows

Expected Outcome:

• Participants can write Python programs, manage the development environment, and perform basic image and data processing.

 

Phase 2: Machine Learning and Deep Learning Fundamentals (3 Days)

Day 4: AI and Machine Learning Fundamentals 

• Artificial Intelligence, Machine Learning, and Deep Learning
• Supervised and unsupervised learning
• Dataset, features, labels, and model predictions
• Classification and regression overview
• AI applications, capabilities, and limitations

Day 5: Deep Learning and Neural Networks 

• Neural network architecture and layers
• Weights, biases, activation functions, and loss functions
• Training, validation, and testing workflows
• CNN fundamentals for image-related tasks
• Pretrained models and transfer learning

Day 6: Model Evaluation and Object Detection Concepts 

• Overfitting, underfitting, and generalization
• Confusion matrix, precision, recall, and F1-score
• Image classification versus object detection
• Bounding boxes, confidence scores, and Intersection over Union (IoU)
• Introduction to object detection evaluation metrics
• Overview of pretrained AI models and inference

Expected Outcome:

• Participants understand the AI model development lifecycle, common evaluation metrics, and the principles behind object detection.

 

Phase 3: AI Development and System Integration (5 Days)

Day 7: AI Application Development and Solution Design

• Understanding the architecture of AI-powered applications
• Mapping real-world problems to suitable AI techniques
• Selecting pretrained models and identifying application requirements
• Understanding input data, preprocessing, inference, and output processing
• Developing modular AI applications using Python
• Case Study: Customer Feedback Analysis using AI
• Hands-on Lab: Build a Python application to process customer feedback and generate meaningful predictions.

Day 8: Pretrained AI Models and Practical Inference 

• Introduction to pretrained models and model reuse
• Loading and executing pretrained models using Python and appropriate AI libraries
• Preparing input data and interpreting model predictions
• Understanding classification, prediction confidence, and model limitations
• Processing multiple inputs and handling invalid data
• Case Study: Automated Document or Image Classification
• Hands-on Lab: Develop an AI application that classifies input data using a pretrained model.

Day 9: Computer Vision and Camera-Based AI Applications

• Introduction to computer vision using OpenCV
• Image acquisition, resizing, preprocessing, and video frame extraction
• USB webcam integration and live camera capture
• Introduction to pretrained YOLO models for object detection
• Understanding bounding boxes, class labels, and confidence thresholds
• Case Study: AI-Based Object Detection for Pest Monitoring or Equipment Inspection
• Hands-on Lab: Develop a camera-based application to detect supported object classes in images, recorded videos, or a live webcam feed.

 Day 10: AI Components and System Integration 

• Designing reusable AI components using Python functions and classes
• Separating AI inference logic from application and business logic
• Defining structured inputs and outputs using JSON and CSV
• Integrating AI predictions into existing application workflows
• Error handling, logging, input validation, and basic performance measurement
• Introduction to library-based and service-based AI integration approaches
• Case Study: Predictive Maintenance Using Sensor Data or AI-Assisted Inspection
• Hands-on Lab: Integrate an AI model into a sample application and use its predictions to support a simple decision-making workflow.

Day 11: End-to-End AI Application Mini-Project 

• Requirements analysis and selection of a real-world AI use case
• Selection and integration of a suitable pretrained model
• Development of the data processing and inference pipeline
• Integration of AI predictions with application logic
• Testing, debugging, and handling common application errors
• Demonstration of the completed AI-enabled application
• Review of system architecture, limitations, and possible enhancements

 Suggested Mini-Projects:

• AI-Based Customer Feedback Analyzer
• Image Classification and Object Detection System
• Camera-Based Pest Monitoring Prototype
• Sensor Data Anomaly Detection for Predictive Maintenance

Expected Outcome:

• Participants will develop a working AI application using a pretrained model, process real-world inputs, and integrate AI predictions into an application workflow. They will gain practical exposure to text analytics, computer vision, and AI system integration.

 

Phase 4: AI Model Deployment, REST APIs, and Web Application Integration (4 Days)

Day 12: AI Inference Application Packaging and Configuration 

• Designing and organizing an AI inference pipeline
• Loading and executing pretrained AI models
• Separating model inference from application and business logic
• Processing different input types, including text, structured data, and images
• Managing model configuration, dependencies, and application settings
• Implementing logging, input validation, and error handling
• Hands-on Lab: Prepare a reusable AI inference application for integration with other software components

Day 13: Building REST APIs with FastAPI 

• Introduction to REST API architecture and FastAPI
• Creating API endpoints for AI inference
• Defining request and response models using Pydantic
• Accepting input data and returning structured AI predictions
• Processing text, JSON, and image inputs, as applicable
• Returning prediction labels, confidence scores, and other relevant results
• API validation, exception handling, and interactive documentation
• Hands-on Lab: Develop a REST API that exposes an AI model as a call able service

Day 14: Web Application and Existing System Integration

• Understanding frontend-to-backend communication
• Calling AI APIs using JavaScript
• Sending application data to an AI inference service
• Displaying AI predictions and processing results in a frontend interface
• Integrating AI capabilities with an existing web application
• Handling API errors, invalid inputs, and inference status
• Hands-on Lab: Integrate an AI inference API into a sample web application

Day 15: Deployment and Real-Time AI Integration Fundamentals 

• Understanding the architecture of an AI-enabled application
• Introduction to deploying AI inference services in a target environment
• Managing application configuration, dependencies, and runtime requirements
• Basic latency measurement and resource optimization
• Introduction to concurrent requests and service reliability
• Introduction to real-time communication using WebSockets and streaming concepts
• Camera-to-inference-to-application integration as an optional use case
• Basic security considerations, logging, and deployment troubleshooting
• Hands-on Lab: Run and test the integrated AI application and evaluate its basic performance

Expected Outcome:

• Participants will package an AI inference application, expose AI functionality through REST APIs, integrate AI predictions into a web application, and understand the fundamentals of deployment, performance evaluation, and real-time integration.

 

Phase 5: End-to-End AI Application Development Capstone Project (5 Days)

Day 16: Requirements Analysis and AI System Design 

• Identify a real-world AI application use case
• Define functional requirements, input data, expected outputs, and success criteria
• Select a suitable pretrained model and AI approach
• Design the system architecture and data processing workflow
• Identify integration requirements, development tools, and computing resources
• Prepare a project plan and test scenarios

Day 17: AI Model Integration and Application Development

• Implement data input and preprocessing pipelines
• Load and integrate the selected pretrained AI model
• Develop the AI inference pipeline
• Process model predictions and generate structured outputs
• Integrate AI predictions with application logic
• Test the AI component using sample datasets, images, text, or sensor data, depending on the selected use case

Day 18: Backend Development and System Integration 

• Develop application modules to execute AI inference
• Integrate the AI component into the selected application workflow
• Implement data exchange using structured formats such as JSON or CSV
• Develop REST API endpoints using FastAPI, where applicable
• Connect the AI application to a frontend or existing software interface, where applicable
• Handle input validation, errors, and application interruptions
• Validate end-to-end application functionality

Day 19: Testing, Evaluation, and Performance Optimization

• Perform functional testing and edge-case testing
• Evaluate AI predictions using appropriate metrics
• Measure inference latency and application response time
• Test with different input data and operating conditions
• Optimize model settings and data processing workflows
• Identify model limitations, integration issues, and outstanding defects
• Document test results and performance observations

Day 20: Final Demonstration and Knowledge Transfer

• Demonstrate the completed AI-enabled application
• Explain the system architecture and AI inference workflow
• Review the application source code and integration components
• Present testing results, evaluation metrics, and performance observations
• Document installation, configuration, and usage instructions
• Identify production-readiness gaps and recommended future enhancements

Expected Outcome:

• A demonstrable proof of concept integrating a pretrained AI model into an application workflow, with appropriate data processing, inference, and system integration. Depending on the selected project, the solution may involve text analytics, image classification, camera-based object detection, or sensor-data analysis. The deliverables include source code, technical documentation, test results, and recommendations for further development.

 

Recommended Technology Stack

• Programming Language: Python
• Data Processing: NumPy and Pandas
• Computer Vision: OpenCV
• Object Detection: Pretrained YOLO-family models
• Deep Learning Framework: PyTorch
• AI Model Integration: Pretrained models and Python-based inference pipelines
• Backend API Development: FastAPI
• Frontend Integration: JavaScript and the client’s existing web framework
• Optional Deployment and Optimization: Docker and ONNX Runtime, depending on project requirements

 

Scope Note:

• This program focuses on AI application development and proof-of-concept integration. Production deployment, guaranteed detection accuracy, and integration into a live business system require separate requirements, validation, and acceptance planning.