Machine Learning & Deep Learning, Big Data Platforms (AWS/GCP/Azure), ML Pipelines & MLOps, Pyspark, Python Programming, Production ML Deployment

Machine Learning, Statistical Modeling, Experimental Design, Python programming language, building and scaling data science solutions in cloud environments, distributed computing frameworks, tuning and evaluating ML models for NLP tasks, advanced data systems, working with large-scale complex datasets

Matplotlib, Machine Learning, Scipy, Git, Pandas, RDBMs, Docker, Spark, Clustering, Kubernetes, Python, Data Science methods, Azure stack, scikit-learn, Statistics, Bayesian regression, tree-based models, NumFOCUS stack

Large Language Models (LLMs), Python, Machine Learning, Deep Learning, Agentic AI, Prompt Engineering, RAG, Vector Databases

statistical data analysis , snowflake , Hypothesis Testing, Tableau, Multivariate Analysis, D3, Docker, Python, BigQuery, Sql, Git, Predictive Modeling, Spark, FastAPI, Kubernetes, drift analysis, data visualization tools, ML data science libraries, RAG architectures, sampling methods, LLMs, Time Series techniques, plotly, Classification, prompt engineering, cloud data platforms

Data Science, Python, Sql, Statistical Analysis, Data Analysis, R

Kafka, Google Cloud, DataFlow, Tensorflow, Flask, Keras, Apache Beam, Kubernetes, Python 3, KubeFlow, Big Query, Pub Sub

data preparation , Machine Learning, Apache Spark, Sql, Rest Api, Hadoop Ecosystem, Pytorch, Docker, Scikit, Statistical Analysis, Python, MLflow, Delta Lake