Anirudh Singh
Development
Rajasthan, India
Skills
Machine Learning (ML)
About
Anirudh Singh's skills align with IT R&D Professionals (Information and Communication Technology). Anirudh also has skills associated with Financial Experts (Insurance and Finance). Anirudh Singh appears to be an entry-level candidate, with 22 months of experience.
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Work Experience
Artificial Intelligence Intern
Intel
April 2023 - August 2023
- • Collaborated with a team of engineers and clients to develop and optimize machine learning models • Conducted data preprocessing, feature engineering, and model selection • Implemented CNN models for deep learning-based Projects, excelling in capturing complex image patterns
Sergeant
National Cadet Corps
April 2022 - February 2024
- • Led and supervised a squadron of Air Wing NCC cadets, fostering a disciplined and cohesive team environment • Organized and conducted training sessions, drills, and parades, showcasing strong leadership skills • Acquired and honed a diverse set of skills, including weapons handling, Aircraft Flying, n effective communication, and navigation • Led the organization of various events, including drills, parades, and community development activities Projects Car Brand Identification System | Python, Tensorflow, Keras, sklearn, matplotlib, seaborn, openCV • Utilized Python libraries such as OpenCV and TensorFlow for image processing and deep learning. • Gathered and preprocessed a diverse dataset of car images from various sources to train the model. • Implemented a Convolutional Neural Network (CNN) architecture for image classification Sentiment Analysis System | Python, Tensorflow, Keras, Sklearn, matplotlib, seaborn, nltk • Conducted Sentiment Analysis on a diverse range of text data sources, including customer reviews from amazon • Employed three different sentiment analysis models to achieve comprehensive sentiment analysis results: VADER, Naive Bayes, and Convolutional Neural Network (CNN). • Utilized the VADER (Valence Aware Dictionary and sEntiment Reasoner) model for quick and rule-based sentiment analysis, providing an initial assessment of sentiment polarity. • Implemented a Naive Bayes classifier, a probabilistic machine learning model, to classify text sentiment with higher accuracy by considering the contextual meaning of words and phrases. • Designed and trained a Convolutional Neural Network (CNN) model for deep learning-based sentiment analysis, which excels at capturing complex patterns and nuances in text data. Cryptocurrency Price Prediction System | Python, Tensorflow, Keras, Sklearn, matplotlib, seaborn • Developed a Cryptocurrency Price Prediction System to forecast the prices of various cryptocurrencies, enabling informed investment decisions. • Employed three distinct predictive models: Linear Regression, Random Forest Regressor, and Long Short-Term Memory (LSTM) neural network, to capture different aspects of price trends. • Gathered and preprocessed cryptocurrency market data, including historical price, trading volume, and market sentiment indicators, for model training and evaluation. Research Work Violence Detection through Deep Learning model in Surveillance • Explored advanced deep learning architectures for violence detection, proposing a CNN+LSTM model that achieved a remarkable accuracy rate of 90%. • Introduced a solution integrating spatial and temporal analyses for nuanced analysis of local motion patterns in real-time surveillance. • Developed a system for swift identification and notification of potential violence, enhancing public safety through timely remote intervention. https://drive.google.com/file/d/1rUUMPX3Ar25Vu8MuLaJcRsdDN-oRFbnP/view?usp=sharing A Collective Approach of Drone Detection System based on Machine Learning and Deep Learning • Investigated diverse machine learning and deep learning models for drone detection, highlighting their practical significance in real-world scenarios. • Identified Recurrent Neural Network (RNN) as the most effective, achieving exceptional accuracy rates of up to 99.85% across different datasets for real-time drone detection. • Emphasized the applicability of the research in defense and surveillance, showcasing notable accuracy rates for machine learning and deep learning models. Tomato Leaf Disease Prediction based on deep learning techniques • Introduced a novel machine learning approach for precise tomato leaf disease detection, addressing threats to crop yield and quality. • Assessed various classifiers, showcasing their varied performance in distinguishing different disease classes, supporting ongoing efforts for improved agriculture. • Highlighted the significant potential of deep learning in enhancing the accuracy and efficiency of tomato leaf disease detection, contributing to advancements in agriculture. https://drive.google.com/file/d/1-H06SlWdQUZLYAcQpiNBxlAwkCAoAL2L/view?usp=sharing
Artificial Intelligence and Machine Learning Intern
IIT Kanpur
May 2023 - June 2023
- • Conducting data preprocessing, feature engineering, and model selection and Deployed machine learning models. • Working with a variety of data types, including text data, image data, and financial data • Collaborating with other researchers and engineers to develop new AI/ML technologies and applications.
Education
