I develop and deploy AI solutions leveraging cutting edge technologies, including NLP, large language models , deep learning, computer vision, and generative AI. My expertise includes designing scalable machine learning models, implementing ReID technology for multi camera tracking systems, and enhancing operations across 230+ retail locations in the USA Metro by T Mobile stores. I also built an LLM based chatbot on WhatsApp using Rasa, Infobip, and Twilio, streamlining onboarding and task management processes. I specialize in real time data visualization with Streamlit, advanced data analysis, and model optimization using PyTorch, TensorFlow, and CUDA. My skillset includes end to end AI/ML pipeline development, predictive analytics, and deploying AI solutions in cloud environments like AWS, and Azure.

Developed a range of applications, including 6 AI-driven projects, collaborating with a talented team of fellows.Utilized technologies such as React, Next.js, TypeScript, and Tailwind to create dynamic and responsive user interfaces.Worked with Node, Firebase, MongoDB, and Prisma to build robust and scalable backends.Integrated advanced AI tools including Gemini API, Groq API, and Pinecone into our projects.Gained insights into the importance of teamwork, rapid adaptation to new technologies, and creative problem-solving.

Bytewise offers fellowships in various tracks, and I have the honour of leading the Data Science track! Our fellowship program spans three months and is designed to provide participants with hands-on experience and deep insights into data science. As the Data Science track lead, I'm excited to guide our fellows through this enriching journey, helping them develop essential skills and knowledge that will empower them in their careers.

Student Ambassadors are a global group of campus leaders who are eager to help fellow students, create robust tech communities and develop technical and career skills for the future.

Honored to be chosen as a Microsoft Learn Student Ambassador (MLSA) after a competitive application process. Proud to represent my university as the first MLSA, dedicated to promoting tech education and community engagement.

Reached a significant milestone in my journey as an MLSA – now an 'Alpha MLSA'! Completed learning paths, dived deep into tech, and expanded my knowledge. Ready to take on new challenges and continue promoting tech education. 🚀

• Data Analysis: Collect and analyse large datasets to extract meaningful insights and identify patterns or trends.• Statistical Modelling: Apply statistical techniques and mathematical models to solve complex problems and make data-driven decisions.• Machine Learning: Develop and implement machine learning algorithms to build predictive models and make accurate predictions or classifications.• Data Visualization: Create visual representations of data using charts, graphs, and interactive dashboards to communicate findings effectively.• Data Cleaning and Preprocessing: Clean and preprocess data by handling missing values, removing outliers, and transforming variables for analysis. • Model Evaluation and Validation: Evaluate the performance of machine learning models, perform model validation, and fine-tune algorithms to improve accuracy.• Data Mining and Feature Engineering: Extract valuable insights from large datasets and engineer relevant features to enhance model performance.• Power BI Dashboards: Creating Power BI dashboards by preprocessing, manipulating, cleaning, extracting, transforming, and loading data.

• Data Analysis: Collect and analyse large datasets to extract meaningful insights and identify patterns or trends.• Statistical Modelling: Apply statistical techniques and mathematical models to solve complex problems and make data-driven decisions.• Machine Learning: Develop and implement machine learning algorithms to build predictive models and make accurate predictions or classifications.• Data Visualization: Create visual representations of data using charts, graphs, and interactive dashboards to communicate findings effectively.• Data Cleaning and Preprocessing: Clean and preprocess data by handling missing values, removing outliers, and transforming variables for analysis. • Model Evaluation and Validation: Evaluate the performance of machine learning models, perform model validation, and fine-tune algorithms to improve accuracy.• Data Mining and Feature Engineering: Extract valuable insights from large datasets and engineer relevant features to enhance model performance.• Power BI Dashboards: Creating Power BI dashboards by preprocessing, manipulating, cleaning, extracting, transforming, and loading data.

Responsibilities:Assist with data collection, cleaning, and preprocessing tasks to prepare data for analysisPerform exploratory data analysis to identify patterns, trends, and insights in dataBuild and test statistical and machine learning models to support business objectivesParticipate in the development of data visualizations and dashboards to communicate findingsCollaborate with cross-functional teams to understand business requirements and translate them into data-driven solutionsContribute to the development of data pipelines and automation workflowsProvide support for ad-hoc data requests and analysis from business stakeholdersStay up-to-date on the latest data science tools, techniques, and industry trendsAssist with the documentation of data processing workflows and analysis methodologiesParticipate in team meetings and presentations to share project progress and recommendationsDevelop proficiency in data analysis software, programming languages, and business intelligence tools

Responsibilities:Project Collaboration: Collaborate with our experienced team members to work on five different machine learning projects, each focusing on different algorithms (linear regression, CNN, K-means clustering, SVM, and Ensemble learning).Algorithm Implementation: Assist in implementing machine learning models using popular libraries and frameworks such as TensorFlow, Keras, and scikit-learn..Data Preprocessing: Clean, preprocess, and transform datasets to ensure the data is suitable for training and testing machine learning models.Model Training and Evaluation: Train machine learning models using appropriate techniques and evaluate their performance using various metrics.Hyperparameter Tuning: Participate in hyperparameter tuning to optimize model performance and enhance the overall efficiency of the algorithms.Requirement Gathering: Work closely with the team to understand project requirements, goals, and objectives.Documentation: Document the progress, methodologies, and results of each project effectively for future reference.Deployment Preparation: Prepare machine learning models for deployment in real-world scenarios, considering scalability and efficiency.Research and Learning: Stay updated with the latest trends and advancements in machine learning to continuously enhance your skills and knowledge.Innovative Problem Solving: Identify challenges and propose innovative solutions to improve the accuracy and robustness of machine learning models.

Responsibilities:Project Development: Collaborate with the team to design, develop, and implement machine learning, computer vision, and NLP projects. Apply deep learning techniques to tackle complex problems and deliver effective solutions.Data Analysis: Analyze and preprocess data from various sources to ensure data quality and suitability for model development. Extract meaningful insights from data through exploratory data analysis (EDA) techniques.Model Building: Develop, test, and optimize machine learning models for predictive and analytical purposes. Utilize OpenCV for computer vision tasks and NLP libraries for natural language processing applications.Model Evaluation: Perform rigorous evaluation and validation of models to ensure their accuracy, robustness, and generalization to real-world scenarios.Deployment and Integration: Collaborate with the engineering team to deploy machine learning models into production systems. Ensure seamless integration with existing applications and services.Version Control: Utilize Git and GitHub to manage project codebase effectively. Commit and track changes, create branches, and merge code contributions to maintain a collaborative and organized development workflow.Continuous Learning: Stay updated with the latest advancements in the field of data science, machine learning, and artificial intelligence. Apply new techniques and methodologies to enhance project performance.Documentation: Document all stages of project development, including data preprocessing, model selection, hyperparameter tuning, and deployment processes. Maintain clear and concise documentation in the project repository on GitHub.

Responsibilities:Project Work: Engage in four distinct data science projects, each designed to challenge and enhance your skills.Breast Cancer Prediction: Develop and fine-tune machine learning models using Python and libraries like scikit-learn to predict breast cancer with high accuracy, potentially contributing to early detection and saving lives.Terrorism Data Analysis: Dive into Exploratory Data Analysis (EDA) on terrorism-related datasets, utilizing data visualization libraries such as Matplotlib and Seaborn to gain insights and present your findings effectively.Speech Emotion Recognition: Utilize machine learning techniques, including TensorFlow or PyTorch, to build models capable of recognizing and classifying emotions in spoken words, exploring the intersection between technology and human emotions.Email Spam Detection: Take on the challenge of building models for Email Spam Detection, understanding the critical role of data science in cybersecurity. Implement algorithms and libraries like NLTK or SpaCy for text analysis.Learning and Growth: Leverage the exceptional support and mentorship provided by CodersCave to enhance your skills in data preprocessing, feature engineering, and model building. Work with data manipulation libraries such as Pandas.Data Ethics: Embrace the ethical responsibility of handling sensitive data in a secure and responsible manner, ensuring the highest standards of data privacy and security.Data Visualization: Develop the power of storytelling through data visualization, effectively communicating your findings to diverse audiences using data visualization libraries like Matplotlib, Seaborn, or Plotly.Collaboration: Work closely with a team of fellow interns, learning the value of teamwork and collaboration in tackling complex problems and delivering impactful solutions.

𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬:1. Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress.2. Managing available resources such as hardware, data, and personnel so that deadlines are met.3. Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability.4. Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world.5. Verifying data quality, and/or ensuring it via data cleaning.6. Supervise the data acquisition process if more data is needed.7. Finding available datasets online that could be used for training.8. Defining validation strategies.9. Defining the preprocessing or feature engineering to be done on a given dataset.10. Defining data augmentation pipelines.11. Training models and tuning their hyperparameters.12. Analyzing the errors of the model and designing strategies to overcome them.13 Deploying models to production.