Self learning

In the "Development of Secure Embedded Systems" specialization, I focused on creating robust embedded solutions, starting with the fundamentals of embedded hardware and operating systems. I then explored web connectivity and security measures essential for protecting embedded systems from vulnerabilities. My studies included the development of real-time systems to ensure timely and reliable responses in critical applications. In the capstone project, I applied these principles to create an autonomous runway detection system for IoT, demonstrating my ability to integrate security and functionality in embedded systems. This specialization honed my problem-solving skills and prepared me for advanced tasks in both academic and industrial settings.

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In my IoT specialization, I delved into the fundamentals of the Internet of Things and embedded systems. I explored the Arduino platform, mastering C programming and interfacing techniques to build responsive hardware projects. I also studied the Raspberry Pi, learning Python to create versatile applications and interact with various sensors and actuators. The capstone project allowed me to integrate these skills, resulting in the design and deployment of a unique IoT device. This practical experience involved programming microcontrollers and connecting them to control and monitor the physical world, equipping me with the expertise to develop innovative IoT solutions.

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As part of the University of Michigan's Python Specialization on Coursera, I had the opportunity to work on a hands-on project that involved taking a location as input, utilizing APIs to retrieve its coordinates, storing this data in a SQL database, and ultimately presenting it on a Flask API-powered map. This practical project not only reinforced my Python programming skills but also showcased your ability to seamlessly integrate different technologies to create a valuable and interactive mapping solution.

I learned skills like  Data structures, Web scrapping/ Web Crawler (urllib, sockets, Beautiful soup), Restful API, Relational Databases (sqllite3) and Data visualizations

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The course "Introduction to Embedded Machine Learning and Computer Vision with Embedded Machine Learning" offered by Edge Impulse on Coursera was a valuable and comprehensive introduction to the exciting fields of embedded machine learning and computer vision. Led by the knowledgeable instructor, Shawn Hymel, this course covered essential topics such as feature extraction, model training, model evaluation, and anomaly detection. It also delved into the practical aspects of deploying machine learning models on embedded systems, a critical skill in today's technology landscape. The course's exploration of Convolutional Neural Networks (CNNs) for image classification, along with concepts like data augmentation and transfer learning, provided a well-rounded understanding of computer vision techniques. Additionally, the course introduced object localization, detection, and segmentation, making it a fantastic foundation for anyone looking to embark on a journey into these rapidly evolving fields.

Finishing 'Build a Modern Computer from First Principles: From Nand to Tetris' on Coursera, led by Professors Shimon Schocken and Noam Nisan from The Hebrew University of Jerusalem, was a transformative experience. This course covered the entire spectrum of computer science, starting with the fundamentals of Boolean Logic and Sequential Logic, progressing through Small Scale Integration, Computer Architecture, Assemblers, Virtual Machines, Compilers, Operating Systems, and Applications. What truly made it exceptional was the hands-on nature of the course, allowing me to actively design and construct these components. This journey has provided me with a profound understanding of how computers work, equipping me with invaluable knowledge and skills to navigate the complex world of modern computing.