Online/ e-LMS
Self Paced
Advanced
3 Weeks
About
The AI for IoT: Intelligent Integration of AI with the Internet of Things course offers a specialized 10-week program designed to integrate Artificial Intelligence with the Internet of Things. Aimed at students and early career professionals in tech-related fields, the course features comprehensive lectures, practical labs, and real-world case studies in sectors like healthcare and urban planning. Participants will learn to design, deploy, and optimize AI-driven IoT solutions, gaining hands-on experience and a certificate of completion that attests to their expertise in this cutting-edge area.
Aim
The course aims to equip students and early career professionals with the essential knowledge and practical skills needed to design, deploy, and optimize AI-driven solutions within the IoT infrastructure. By focusing on hands-on applications and real-world case studies across multiple industries such as healthcare, urban planning, and manufacturing, participants will learn to implement advanced AI algorithms that enable IoT devices to perform automated decision-making, improve operational efficiency, and introduce new services. The course aims to foster a deep understanding of the synergistic potential of AI and IoT technologies, preparing participants to lead and innovate in this dynamic and technically demanding field.
Program Objectives
Program Structure
Module 1: Introduction to AI and IoT
Section 1.1: Overview of IoT and AI
- Subsection 1.1.1: What is IoT?
- Definition of IoT and its ecosystem.
- Core components: Sensors, Actuators, Gateways, and Devices.
- Real-world applications: Smart Homes, Industrial IoT (IIoT), and Healthcare.
- Subsection 1.1.2: What is AI?
- What AI means: Definitions and examples.
- Key AI techniques: Machine Learning, Deep Learning, and Neural Networks.
- Applications: Data insights, decision-making, and automation.
Section 1.2: Synergy Between AI and IoT
- Subsection 1.2.1: How AI Enhances IoT
- Benefits of AI in IoT: Improved analytics, real-time insights, and automation.
- Predictive maintenance: Identifying issues before failures occur.
- Real-time analytics: Making IoT smarter with AI.
- Subsection 1.2.2: AI in IoT Use Cases
- Smart Cities: Traffic management and energy optimization.
- Autonomous Vehicles: Real-time decisions and navigation.
- Wearables: Health monitoring and activity tracking.
Section 1.3: Tools and Frameworks for AI and IoT Development
- Subsection 1.3.1: Hardware Overview
- Overview of IoT hardware: Raspberry Pi, Arduino, and Edge devices.
- Hands-on setup for IoT prototyping.
- Subsection 1.3.2: Software Tools
- Introduction to AI frameworks: TensorFlow, PyTorch, and Scikit-Learn.
- IoT communication protocols: MQTT and CoAP.
Module 2: IoT Data Processing and Analytics
Section 2.1: Understanding IoT Data
- Subsection 2.1.1: Types of IoT Data
- Structured data: Sensor readings, logs, and metrics.
- Unstructured data: Images, videos, and audio.
- Common data formats: JSON, CSV, and Protocol Buffers.
- Subsection 2.1.2: Challenges in IoT Data Processing
- Scalability: Handling large-scale IoT data.
- Latency: Real-time data processing.
- Security: Protecting IoT data from breaches.
Section 2.2: Data Acquisition and Preprocessing
- Subsection 2.2.1: Collecting Data from IoT Devices
- Connecting sensors to IoT gateways.
- Streaming data to cloud platforms.
- Subsection 2.2.2: Preprocessing Data for AI Models
- Cleaning data: Removing duplicates and handling missing values.
- Normalization and standardization techniques.
- Encoding categorical data for AI processing.
Section 2.3: Edge and Cloud Computing for IoT
- Subsection 2.3.1: Edge AI: Processing Data Locally
- Benefits: Reduced latency and enhanced privacy.
- Deploying AI models on edge devices like Raspberry Pi.
- Subsection 2.3.2: Cloud AI: Using Platforms
- Platforms: AWS IoT, Google Cloud IoT, and Azure IoT Hub.
- Cloud-based data storage and model deployment.
Module 3: Applying AI to IoT Data
Section 3.1: Machine Learning for IoT
- Subsection 3.1.1: Supervised Learning for IoT Applications
- Using labeled IoT data to predict outcomes.
- Example: Predictive maintenance of industrial machines.
- Subsection 3.1.2: Unsupervised Learning for IoT Applications
- Detecting patterns in IoT data with clustering.
- Example: Identifying anomalies in sensor readings.
Section 3.2: Deep Learning for IoT
- Subsection 3.2.1: Neural Networks for Time-Series Data
- Applying RNNs and LSTMs to predict future trends.
- Example: Energy consumption forecasting.
- Subsection 3.2.2: Computer Vision in IoT
- Using CNNs for image recognition in IoT devices.
- Example: Object detection in smart surveillance systems.
Section 3.3: Reinforcement Learning in IoT
- Subsection 3.3.1: Applications of RL in IoT
- Dynamic optimization of IoT systems like smart grids.
- Subsection 3.3.2: Implementing RL Models for IoT
- Training and deploying RL models for decision-making systems.
Module 4: AI Deployment in IoT Systems
Section 4.1: AI at the Edge
- Subsection 4.1.1: Advantages and Challenges of Edge AI
- Benefits: Faster decision-making, reduced latency, and improved privacy.
- Challenges: Limited computational power on edge devices.
- Subsection 4.1.2: Deploying AI Models on Edge Devices
- Using TensorFlow Lite for deploying AI models.
- Optimization techniques for edge devices.
Section 4.2: AI in the Cloud
- Subsection 4.2.1: Integrating AI with Cloud IoT Platforms
- Connecting IoT devices to cloud services for advanced analytics.
- Examples: Azure IoT Hub, AWS IoT Core.
- Subsection 4.2.2: Creating Scalable and Secure AI-IoT Solutions
- Implementing best practices for scalable deployments.
- Security considerations for cloud-integrated AI-IoT systems.
Module 5: IoT Security and Ethical Considerations in AI
Section 5.1: IoT Security Challenges
- Subsection 5.1.1: Threats in IoT Ecosystems
- Common issues: Data breaches, device hijacking, and DDoS attacks.
- Subsection 5.1.2: AI’s Role in IoT Security
- Using AI for intrusion detection and threat prevention.
Section 5.2: Ethics in AI for IoT
- Subsection 5.2.1: Data Privacy and Bias Concerns
- Ensuring ethical practices in IoT data collection and processing.
- Subsection 5.2.2: Transparent and Explainable AI
- Building interpretable models to foster trust and compliance.
Participant’s Eligibility
The course is specifically designed for:
- Students:
- B.Tech, M.Tech, and M.Sc students who are specializing in fields such as Computer Science, Information Technology, and Electronics. This course is suitable for those at an intermediate to advanced level of their technical education who are looking to deepen their understanding of AI and IoT technologies.
- Early Career Professionals:
- Individuals who are in the early stages of their careers in IT services, consulting, and Fintech IT services. This course is ideal for professionals seeking to enhance their skills and knowledge in cutting-edge technologies and looking to apply AI solutions in real-world IoT scenarios.
Program Outcomes
- Mastery of AI and IoT Concepts: Understand the fundamental and advanced concepts of AI and IoT, including their integration, applications, and impact on various industries.
- Practical Skills Development: Gain hands-on experience designing, implementing, and optimizing AI-driven IoT solutions through interactive labs and project-based learning.
- Problem-Solving Abilities: Enhance problem-solving skills by tackling real-world challenges and creating innovative solutions within the AI and IoT frameworks.
- Industry Readiness: Be prepared for the workforce with up-to-date knowledge and skills that are highly sought after in the tech industry.
- Professional Networking: Build connections with industry professionals, instructors, and peers that can lead to future career opportunities and collaborations.
- Research and Development Skills: Develop the ability to conduct rigorous research and contribute new knowledge or innovations in the field of AI IoT.
- Certification and Recognition: Earn a prestigious certificate of completion that validates the expertise gained and stand out in job markets; plus, the opportunity for outstanding work to be formally recognized.
- Strategic Insight: Acquire strategic insights into integrating AI technologies with IoT systems to drive business efficiency and innovation.
Fee Structure
Fee: INR 8,499 USD 112
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
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Key Takeaways
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Placement Assistance
Corporate Networking Events
Resume and Interview Preparation
Corporate Guest Lectures
Alumni Network
Future Career Prospects
- IoT Solution Architect: Design and manage comprehensive IoT solutions.
- AI Systems Developer: Develop AI algorithms and models to enhance IoT systems.
- Data Scientist for IoT: Analyze IoT data for actionable insights.
- Cybersecurity Analyst for IoT: Implement security measures for IoT networks.
- IoT Project Manager: Lead IoT project initiatives.
- Industrial IoT Engineer: Optimize industrial processes using IoT technologies.
- Smart City Technology Coordinator: Manage and improve urban IoT applications.
- AI and IoT Research Analyst: Research new advancements in AI for IoT.
- IoT Business Development Manager: Drive business growth through IoT innovations.
- Consultant for AI and IoT Integration: Advise on integrating AI with IoT in business strategies.
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