

Data Scientist familiar with gathering, cleaning and organizing data for use by technical and non-technical personnel. Advanced understanding of statistical, algebraic and other analytical techniques. Highly organized, motivated and diligent with significant background in cloud computing,deep learning and machine learning models.
Machine Learning: Supervised - Regression (Linear Regression), Classification (Logistic Regression, Decision Tree), Unsupervised - Clustering (k - Means), Dimensionality Reduction (PCA), Ensemble Model - Bagging (Random Forest), Boosting
Deep Learning: Neural Networks (Tensorflow,Pytorch)
NLP: Spacy , NLTK,BERT,GPT3
Programming Languages: Python, R
Statistics: Probability, Sampling, CLT, Inferential StatisticsTools : NumPy,Pandas,scikit-learn,Matplotlib,Seaborn,Tensorflow,PySpark
Deployment: Azure ML Studio, Docker, K8's,AWS Sage Maker, ML fLow, Mini kube ,Tensor Serving
Version Control: GitHub, Azure DevOps
Database: SQL
Computer Vision : OpenCV
Visualization: Power BI
CI/CD: Git Actions , Circle Ci
R Programming Course for Data Science
R Programming Course for Data Science