1. Prediction of Movie Genre by Fine-tunning GPT (Code & Document)
2. Fine-tunning BERT for Fake News Detection (Code & Document)
3. Covid Tweet Classification by Fine-tunning BART (Code & Document)
4. Semantic Search Using BERT (Code & Document)
5. Abstractive Semantic Search by OpenAI Embedding (Code & Document)
6. Fine-tunning GPT for Style Completion (Code & Document)
7. Extractive Question-Answering by BERT (Code & Document)
8. Fine-tunning T5 Model for Abstract Title Prediction (Code & Document)
9. Image Captioning by Fine-tunning ViT (Code & Document)
10. Build Serverless ChatGPT API (Code & Document)        
11. Statistical Analysis In Python (Code & Document)
12. Clustering Algorithms (Code & Document)         
13. Customer Segmentation (Code & Document)
14. Time Series Forecasting (Code & Document)​                                          
15. Predict Customer Churn (Code & Document)
16. Classification with Imbalanced Classification (Code & Document)          
​17. Feature Importance (Code & Document)
18. Feature Selection (Code & Document) 
19. Text Similarity Measurement (Code & Document)
20. Dimensionality Reduction (Code & Document)
21. Prediction of Methane Leakage (Code & Document)
22. Imputation by LU Simulation (Code & Document)
23. Histogram Uncertainty (Code & Document)
24. Declustering to Improve Preferential Sampling (Code & Document)
25. Uncertainty in Spatial Correlation (Code & Document)
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1. Machine Learning Overview (Code & Document)                
2. Python and Pandas (Code & Document)
3. Main Steps of Machine Learning (Code & Document)
4. Classification (Code & Document)
5. Model Training (Code & Document)
6. Support Vector Machines (Code & Document)
7. Decision Trees (Code & Document)
8. Ensemble Learning & Random Forest (Code & Document)
9. Artificial Neural Network (Code & Document)
10. Deep Neural Network (Code & Document)
11. Unsupervised Learning (Code & Document)
12. Multicollinearity (Code & Document)
13. Introduction to Git (Code & Document)
14. Introduction to R (Code & Document)
15. SQL Basic to Advanced Level (Code & Document)
16. Develop Python Package (Code & Document)
17. Introduction to BERT LLM (Code & Document)
18. PySpark Fundamentals for Big Data (Code & Document)
19. Exploratory Data Analysis (Code & Document) 
​20. Object Oriented Programming in Python (Code & Document)
​21. Natural Language Processing (Code & Document) 
​22. Convolutional Neural Network (Code & Document)

Peer Reviewed Papers


1- Rezvandehy, M. & Mayer, B. (2023). Machine Learning Approaches for the Prediction of Serious Fluid Leakage from Hydrocarbon Wells. Data-Centric Engineering, Cambridge University Press. https://doi.org/10.1007/s11053-018-9418-z
2- Rezvandehy, M. & Juliana Y. Leung, Weishan Ren, Ben Hollands, Guoai Pan. (2018). An Improved Workflow for Permeability Estimation from Image Logs with Uncertainty Quantification. Natural Resources Research, Springer. https://doi.org/10.1017/dce.2023.9?
3- Rezvandehy, M. & Deutsch, C.V. (2017). Horizontal variogram inference in presence of widely spaced well data. Petroleum Geoscience, EAGE. http://dx.doi.org/10.1144/petgeo2016-161
4- Rezvandehy, M. & Deutsch, C.V. (2017). Declustering Experimental Variograms by Global Estimation with Fourth Order Moments. Stochastic Environmental Research and Risk Assessment, Springer, 1-17.   https://doi.org/10.1007/s00477-017-1388-x
5- Rezvandehy, M. & Deutsch, C.V. (2017). Geostatistical Modeling with Histogram Uncertainty: Confirmation of a Correct Approach. Natural Resources Research, Springer, 1-18.  https://doi.org/10.1007/s11053-016-9322-3
6- Rezvandehy, M. (2017). Geostatistical Reservoir Modeling with Parameter Uncertainty in Presence of Limited Well Data. University of Alberta, PhD Thesis.  https://doi.org/10.7939/R3MG7G74S
7- Rezvandehy, M. (2014). Logical depth modeling of a reservoir layer with the minimum available data-integration geostatistical methods and seismic attributes. Journal of Unconventional Oil and Gas Resources, Elsevier, 7, 11-21.  https://doi.org/10.1016/j.juogr.2014.03.003
8- Rezvandehy, M., Aghababaei, H., & Raissi, S. T. (2011). Integrating seismic attributes in the accurate modeling of geological structures and determining the storage of the gas reservoir in Gorgan Plain (North of Iran). Journal of Applied Geophysics, Elsevier, 73(3), 187–195.   https://doi.org/10.1016/j.jappgeo.2010.12.008

Conference and Technical Reports


1- Rezvandehy, M. & Leung, J. (2017). Optimum Permeability Modeling with Image Logs. Presented at CSPG Canada‘s Energy Geoscientists, Calgary, Canada.
2- Rezvandehy, M. & Deutsch, C.V. (2016). Declustering Experimental Variograms by Global Estimation with Fourth Order Moments. CCG Paper 2016-110,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
3- Rezvandehy, M. & Deutsch, C.V. (2016). Workflow for Variogram Parameter Uncertainty in Petroleum Reservoir and Needed Fortran Codes. CCG Paper 2016-119,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
4- Rezvandehy, M. & Deutsch, C.V. (2016). Hydrocarbon Initially in Place with Full Uncertainties in Presence of Limited Data. CCG Paper 2016-121,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
5- Rezvandehy, M. & Deutsch, C.V. (2016). A Fortran Code for Variogram Declustering and Examples. CCG Paper 2016-403,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
6- Rezvandehy, M. & Deutsch, C.V. (2016). A Fortran Code for Estimation Correct Variogram Uncertainty and Transfer to Geostatistical Modeling. CCG Paper 2016-404,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
7- Rezvandehy, M. & Deutsch, C.V. (2015). Improved Variogram Estimation with Multiple Data Types. CCG Paper 2015-129,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
8- Rezvandehy, M. & Deutsch, C.V. (2015). Direct Observation of Parameter Uncertainty. CCG Paper 2015-130,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
9- Rezvandehy, M. & Deutsch, C.V. (2015). Overview of Copulas in Geostatistics. CCG Paper 2015-136,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
10- Rezvandehy, M., Lajevardi S. & Deutsch, C.V. (2015). Automatic Regridding to Fill Flow Simulation Grid Blocks. CCG Paper 2015-209,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
11- Rezvandehy, M. & Deutsch, C.V. (2015). Automatic Regridding to Fill Flow Simulation Grid Blocks. CCG Paper 2015-209,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
12- Rezvandehy, M., Khan, D. & Deutsch, C.V. (2015). ParUnce: Code for Parameter Uncertainty. CCG Paper 2015-408,   Centre for Computational Geostatistics, Edmonton, Canada.
13- Rezvandehy, M. & Deutsch, C.V. (2014). A New Program for Calculating Variogram of Gridded Data. CCG Paper 2014-408,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
14- Rezvandehy, M. & Deutsch, C.V. (2014). Combining Multivariate Gaussian Distributions from Different Sources. CCG Paper 2014-127,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
15- Rezvandehy, M. & Deutsch, C.V. (2014). Theoretical and Numerical Framework for Improving Variogram of High Resolution Well data by Different Sources. CCG Paper 2014-206,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
16- Rezvandehy, M. & Deutsch, C.V. (2014). Practice of Improved Variogram Estimation with Sparse Data. CCG Paper 2014-207,   Centre for Computational Geostatistics, University of Alberta, Edmonton, Canada.
17- Rezvandehy, M., Aghababaie, H., Raissi, S. T. & Kushavand, B. (2007). Application of Seismic Attribute in Geological Modeling of GORGAN Reservoir.   Presented in 25th Symposium on Geosciences GSI, Iran, February 2006.

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