

Internship on Python AIoT Application Development with APSCHE and PHYTEC
Gain hands-on experience in the cutting-edge fields of Embedded Systems and Artificial Intelligence of Things (AIoT) through this exciting internship. Jointly organized by APSCHE and PHYTEC.
Duration: 16 Weeks
Course Structure:
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8 Weeks: Hands-on Training
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6 Weeks: Project Development
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2 Weeks: Interview Preparation and Personality Development
Detailed Course Content
Week 1-8: Hands-on Training Week 1: Introduction to AIoT and Python Basics • Overview of AIoT (Artificial Intelligence of Things) • Python Basics: Syntax, Data Structures, Functions, and Modules • Setting Up Python Environment (IDEs, Virtual Environments) • Introduction to IoT Protocols: MQTT, HTTP, CoAP Week 2: IoT Fundamentals • Understanding Sensors, Actuators, and Microcontrollers • Interfacing with IoT Hardware (Raspberry Pi, Arduino) using Python • Communication Protocols: UART, SPI, I2C • Reading Data from Sensors Using Python Week 3: Networking and IoT Communication • Setting Up IoT Devices on a Local Network • Implementing MQTT with Python (Publisher/Subscriber Model) • Creating RESTful APIs for IoT Devices • Secure Communication using TLS/SSL Week 4: AI and Data Basics • Introduction to Machine Learning and AI • Data Collection and Preprocessing for AIoT Applications • Exploring Python Libraries: NumPy, Pandas, Matplotlib • Basics of Cloud Storage and Databases for IoT Data Week 5: Edge AI with Python • Understanding Edge AI and its Importance in AIoT • Using TensorFlow Lite and PyTorch for Edge Devices • Training and Deploying Simple Machine Learning Models on IoT Devices • Case Study: Predictive Maintenance on IoT Data Week 6: IoT Cloud Integration • Introduction to IoT Cloud Platforms (AWS IoT, Azure IoT, Google IoT) • Sending IoT Data to the Cloud using Python • Processing and Analyzing Cloud Data • Building Real-Time Dashboards with Python Week 7: Advanced AIoT Development • Building AI Models for IoT Applications (e.g., Anomaly Detection) • Image and Audio Processing for AIoT Devices • Real-Time AI Inference on IoT Devices • Case Study: Smart Home Automation with AI Week 8: Debugging and Optimization • Debugging IoT Systems and Python Code • Optimizing Python Applications for Resource-Constrained IoT Devices • Power Management in AIoT Applications • Review and Preparation for Project Development
Week 9-14: Project Development Week 9: Project Ideation and Planning • Brainstorming and Finalizing AIoT Project Ideas • Writing a Project Proposal and Requirements Document • Setting Up the Development Environment Week 10: System Design and Prototype Development • Designing System Architecture • Implementing Initial Prototypes of IoT and AI Components • Testing Basic Functionality Week 11: AI Model Development and Integration • Building and Training AI Models • Integrating AI Models into IoT Devices • Testing Model Performance on Real-Time IoT Data Week 12: Cloud and Dashboard Development • Setting Up Cloud Integration for Data Storage and Processing • Developing Real-Time Dashboards for Data Visualization • Connecting AIoT Devices to the Cloud Week 13: System Testing and Optimization • End-to-End Testing of AIoT Systems • Optimizing System Performance (AI Inference, Data Transfer, and Processing) • Refining the User Interface (if applicable) Week 14: Final Project Demonstration • Preparing Final Project Presentation • Delivering a Demonstration of the Project Functionality • Peer Review and Feedback
Week 15-16: Interview Preparation and Personality Development Week 15: Interview Preparation • Commonly Asked Questions in Python and AIoT Interviews • Mock Interviews (Technical and Behavioral) • Problem-Solving Techniques and Code Review Week 16: Personality Development • Building Confidence for Interviews • Enhancing Communication and Presentation Skills • Resume Writing and Portfolio Development • Team Collaboration and Leadership Skills
Learning Outcomes By the end of this course, participants will: 1. Gain proficiency in Python for AIoT development. 2. Build and deploy AIoT applications involving sensors, AI models, and cloud integration. 3. Have a comprehensive project to showcase their skills. 4. Be well-prepared for technical interviews and possess improved interpersonal skills.