Vignesh Bharadwaj M

Hello! I'm Vignesh Bharadwaj M, an enthusiastic and driven individual with a passion for knowledge and creativity. Tech-savvy professional with experience in Artificial intelligence and Machine Learning. Enjoys solving real-world problems and working with teams to create effective solutions. With a positive attitude and a commitment to personal growth.

MICRO LABS LTD, 

• Digital Marketing Using AI, Assisted in the development and implementation of AI-driven digital marketing strategies to enhance customer engagement.
• Analyzed large datasets to identify trends and insights for targeted marketing campaigns.
• Utilized AI tool(Tableau Public) to provide data visualizations to analyze and worked on where there are losses., 04/2024, Bengaluru, India

SMART PLANT MONITORING SYSTEM

• Designed and implemented a smart plant monitoring system using IoT hardware.
• Utilized sensors to monitor soil moisture, temperature, and light levels for optimal plant care.
• Developed a microcontroller-based system using Arduino/Raspberry Pi for data collection and processing.
• Implemented automated watering and lighting controls based on sensor data to maintain ideal growing conditions.

AUDIO EMOTION ANALYSIS

• Developed an audio emotion analysis system to detect and classify emotions from voice recordings.
• Implemented machine learning models using Python, TensorFlow, and Librosa for feature extraction and classification.
• Collected and preprocessed audio datasets, ensuring high-quality data for training and testing models.
• Achieved high accuracy in emotion classification through model optimization and hyperparameter tuning.

PRODUCT RECOMMENDATION USING BODY MEASUREMENTS

• Developed a personalized product recommendation system using body measurements.
• Utilized Python, SMPL, and EKF for data processing and model training.
• Designed methods for accurate data collection and standardization of user measurements.
• Integrated recommendation algorithms with an existing e-commerce platform(Amazon).
• Conducted A/B testing to improve recommendation accuracy and user satisfaction.