About Me :
I am Kamlesh Khatod, a Computer Engineering graduate from MVPS’s KBTCOE in Nashik, holding an Honours Degree in Artificial Intelligence and Machine Learning. My passion lies in applying AI to solve real-world problems, and I am always looking for opportunities to grow and innovate.
My Experience:
As a Backend Web Developer at Weoto Technologies, I designed a web application for creating and downloading invoices. I used NestJs and PostgreSQL with the TypeORM package to handle data and manage the database. This experience from December 2023 to July 2024 allowed me to build practical skills in backend development.
Key Projects & Skills
I developed “Anvīksa,” (Copyright issued by Govt. of India) a machine learning-based system that refines CAPTCHA by passively detecting bots. This solution uses JavaScript to capture user behavior like mouse movements and keypresses, which is then classified by machine learning models like Random Forest and XGBoost to detect bot activity. I also developed “CodeArena,” a web app where users can practice coding in languages like OOP, DSA, and SQL. The app includes a code compiler and predefined test cases to check code correctness and features a one-day session timeout for security.
My technical skills include proficiency in languages such as C++, Java, Python, HTML, and JavaScript, as well as frameworks and libraries like NestJs, Pandas, Numpy, and Tensorflow.
I have also completed certifications in Web Development and Machine Learning from Internshala and Stanford University/DeepLearning.Ai, respectively.
Skills
Experience Level
Language
Work Experience
Education
Qualifications
Industry Experience
- Backend & Database: NestJs, PostgreSQL
- Core Languages: JavaScript, Python
- ML Libraries: Pandas, Numpy, Tensorflow
Anvīksa: The Future of Web Security
Anvīksa is a novel, machine learning-based solution designed to revolutionize web security.
It replaces traditional CAPTCHA puzzles with a seamless and passive bot detection system, enhancing both security and user experience.
Core Functionality
Passive Bot Detection: The system captures user behaviors such as mouse movements, keypresses, and scrolls via JavaScript to identify bot activity without interrupting the user’s flow.
Intelligent Analysis: Behavioral data is classified using advanced machine learning models, including Random Forest and XGBoost, to accurately detect bot activity.
Originality: The literary work for this project is copyrighted, reflecting its novel approach to web security.
Technical Stack
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