About Me
I am Mohneet Sandhu, a dedicated Data Science professional pursuing my Master's degree at the University of Maryland, College Park, with a targeted graduation in 2026. I have a solid foundation in Applied Statistics and Analytics from NMIMS, where I developed a rigorous understanding of statistical inference, probabilistic modeling, and the mathematical underpinnings of machine learning algorithms.
My core competencies lie in designing and deploying advanced deep learning models, such as Convolutional Neural Networks (CNNs) for computer vision tasks and Recurrent Neural Networks (RNNs) and Transformers for natural language processing. I specialize in implementing state-of-the-art AI techniques, from transfer learning and generative adversarial networks (GANs) to reinforcement learning and self-supervised learning, using frameworks like PyTorch, TensorFlow, and Keras. I am also skilled in feature engineering, dimensionality reduction, and hyperparameter optimization to enhance model performance and scalability.
I am experienced in managing end-to-end machine learning pipelines, leveraging tools such as AWS for cloud computing, Docker for containerization, and Git for version control and collaboration. I have a deep understanding of big data ecosystems, including Hadoop and Spark, and I am adept at SQL and NoSQL databases, enabling robust data extraction, transformation, and loading (ETL) processes.
Passionate about leveraging the latest in AI and machine learning, I thrive on solving complex, data-centric problems and am committed to driving impactful innovations across industries.
Skills And Expertise:
Programming Languages: Python, R, SQL, SAS.
Machine Learning and Deep Learning:
Deep Learning & Neural Networks: TensorFlow, PyTorch, Keras.
Specialized Techniques: LSTM, RNN, CNN, Transformers, GANs.
Natural Language Processing: NLTK, spaCy, BERT, GPT.
Generative AI: GPT, Gemini.
Data Analysis & Statistical Modeling: Hypothesis Testing, Data Cleaning and Preprocessing, Predictive Modeling, Time Series Analysis, A/B Testing.
Data Visualization: Tableau, Power BI, Google Data Studio, Matplotlib, Seaborn
Cloud Computing: AWS EC2, S3, Lambda, IAM, CloudWatch, Elastic Beanstalk, etc.
Frameworks: Flask, FastAPI.
Collaborative Tools: Jupyter Notebook, Google Colab, RStudio, GitHub, GitLab
Big Data Technologies: Hadoop, Spark, Apache Kafka, Apache Hive
Data Engineering: ETL Processes, Data Pipelines, Data Warehousing, SQL and NoSQL Databases, Airflow
DevOps and Version Control:
DevOps: CI/CD, Docker.
Version Control: Git, GitHub, Bitbucket.
Python for Computer Vision with OpenCV and Deep Learning
Foundational Generative AI by Ineuron.
Deep Learning Specialization by DeepLearning.AI
SQL-MySQL for Data Analytics and Business Intelligence
Python for Data Science and Machine Learning Bootcamp
NLP - Natural Language Processing with Python
R Programming A-Z: R for Data Science with Real Exercises
Advanced Foundations of Python Programming
Git and Github Ctash Course
The Web Developer Bootcamp 2024