Machine Lerning & Statisticl Analysis
Machine Learning Algorithms
- Decision Trees
- Regularization (Lasso, Ridge, Elastic Nets)
- Ensembles (Bagging, Boosting, Stacking)-
- Naïve Bayes
- KNN
- SVM
- Neural Nets
- Deep Learning
- Text Mining and NLP
- Computer Vision
- Dimension Reduction (PCA, t-SNE)
- Clustering
Statistical Analysis
- GLM (including GLMM)
- Time Series
- Survival Analysis
- Bootstrapping
- Spatial Analysis and GIS.
Software
- Python (Numpy, SciPy, Pandas, matplotlib, Seaborn, Scikit-Learn, XGBoost, Theano, TensorFlow, OpenCV, NLTK, Statsmodels, Beautiful Soup), R (familiar)
- SQL (Oracle, SQLite, PostgreSQL, Proc SQL)
- SAS (Base, Macro, STAT, ETS)
- Spark (PySpark, Spark SQL)
- Hadoop (Hive)
- Jupyter Notebook
- Atom
- Unix shell script (awk, sed, grep, SSH)
- Linux Server (Ubuntu, CentOS)
- Mac, Windows
- AWS (EC2, S3, Redshift)
- Git, GitHub
- H20
- LaTex, Markdown
- SAS Enterprise Guide
- SAS Enterprise Miner
- JMP
- Emblem, Classifier
- QGIS
