Hi , I'm Greg

A passionate Machine Learning Engineer / Data Scientist

🔭 I’m currently working on Azure DP-100 Data Scientist Associate Certification
🌱 I’m currently learning Reinforcement Learning
👨‍💻 All of my projects are available at linkedin.com/in/greg-h-mehdiyev-1422b533
💬 Ask me about PyTorch, Machine Learning, Software Engineering
📫 How to reach me [email protected]
📄 Know about my experiences linkedin.com/in/greg-h-mehdiyev-1422b533

Programming Languages

Python | C | C++ | Bash | Matlab | Java | JavaScript | R

Machine Learning

PyTorch | TensorFlow | DeepNet | YOLO | RCNN | SSD | Transformers | BART | BERT | GPT | Word2Vec | NLTK | LSTM | GRU | HMM | Numpy | Pandas

SQL and NoSQL

MySQL | PostgreSQL | Oracle | Cassandra | MongoDB | AWS DynamoDB

Cluster Computing

Apache Hadoop | PySpark | ETL | Kafka | Entity Resolution

Cloud Services

AWS EMR | AWS S3 | AWS Redshift | AWS Spectrum | AWS EC2 | AWS CLI | AWS EKS | Azure Databricks | Power BI

Frameworks and Libraries

React | Ajax | Istio | Grafana | Prometheus

DevOps

NodeJS | Docker | Kubernetes | CI /CD | Gatling | Hive | Presto | Grafana | Istio

Greg Mehdiyev's Projects

The goal of this project is to design and train deep convolutional neural networks using PyTorch. I will design a deep net architecture to classify (small) images into 100 categories and evaluate the performance of the architecture by uploading the predictions to the Kaggle competition.

grocerysegmentai

GrocerySegmentAI is a platform as a service for grocery businesses. This product provides a way for these businesses to upload customer data and receive accurate predictions on which customers will buy a specific product on their next order.

musicstreamingservice

Open-source music streaming platform that uses microservices, Docker, Flask and DynomoDB.

rcnnsateliteairplanesegmentation

The goal of this project is to build a instance segmentation model that is able to detect and segment airplanes from the satelite images from the airporst around the world.

realtimestreaming

A cloud service that collects buyer property preference data in real-time, puts it through a data streaming pipeline and feeds it into the compute layer, where data is analyzed in the ML pipeline and property recommendations are adjusted based on new buyer preferences.