Abhinav Ram Seggyam

Passionate graduate, aspiring to kick-start my career as an engineer/researcher in the field of AI and its’ subsets such as Deep Learning, NLP and Image Processing. Proficient in Python, various data analytical (Pandas) and visualization libraries (Seaborn, Matplotlib) and techniques, building predictive models, and employing advanced algorithms to extract meaningful insights. Gained invaluable experience in handling data of different formats and solving real world problems, at CDAC, Pune. Possessing strong communication skills, leadership experience, and a collaborative mindset, I excel in guiding teams, sharing ideas, and ensuring seamless project execution. Committed to building intelligent systems, explore and apply various pre-trained models to solve complex challenges. Looking forward to combining technical skills with a passion for AI to contribute to cutting-edge solutions while continuously growing as a professional.

• Malicious URL Detection

Developed a machine learning-based solution to identify and classify malicious URLs to enhance cybersecurity. Collected and preprocessed datasets of benign and malicious URLs, extracting features like URL patterns, domain reputation, and metadata. Designed and trained classification models using algorithms such as Random Forest and Gradient Boosting, achieving high detection accuracy. 

• Drowsiness Detection System

Developed a real-time drowsiness detection system using computer vision and machine learning to enhance road safety. Implemented facial landmark detection to monitor eye blink rates and yawning patterns. Utilized algorithms like HOG and DNN-based models for facial feature extraction and drowsiness classification. Achieved high accuracy in identifying driver fatigue and provided real-time alerts to prevent accidents.

• Sentiment Analysis of Customer Reviews

The project aims to develop a machine learning model that can analyze  and classify customer reviews into positive, negative, or neutral  sentiments. This solution can help businesses understand customer  feedback, improve products or services, and enhance customer  satisfaction. 

• NLP-Based News Article Classification System

To develop a Natural Language Processing (NLP)-based model that can  automatically classify news articles into predefined categories like  Politics, Sports, Technology, Entertainment, and Business. The project  leverages NLP techniques to preprocess text data and uses machine  learning models for classification.  

• Text Summarization Using Natural Language Processing

To build a system that automatically summarizes large text documents or  articles into concise summaries while retaining key information and  insights. The system especially takes advantage of pre-trained transformers like BERT, Llama and GPT and fine tune them to make our work easier. 

• AWS Cloud Architecting, Eduskills
• AWS Machine Learning, Eduskills
• AWS Cloud Foundations, Eduskills
• Google Mobile Development using Kotlin, Eduskills

• Basketball
• Cycling
• Travelling
• Gaming