Dr. Ammar Alhaj, Machine Learning (AI) instructor
PhD in Engineering Informatics from Tomas Bata University in Zlin (5 years), Czech Republic.
My thesis was: Fault Tolerance for Big Data Scientific Workflows in Cloud Computing Environments.
LICENSES & CERTIFICATIONS
1-Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization – DeepLearning.AI
The certification issued Jan 2021 -Credential ID: TALS96R2M8AA
2-Applied Machine Learning in Python – Michigan University
The certification issued Aug 2020 – Credential ID: MA4PTU9JV698
3-Neural Networks and Deep Learning – DeepLearning.AI
The certification issued Sep 2020 – Credential ID ZJLB72TXAAVE
4-Big Data Modeling and Management Systems – California, San Diego University
The certification issued Jun 2020 Credential ID: TZTJ3MJ75H7U
5-Introduction to Data Science in Python- Michigan University
The certification issued Jun 2020-Credential ID HNCZCECD8Z78
6-Machine Learning With Big Data – California, San Diego University
The certification Issued Jul 2020- Credential ID E9SFWJULJL73
7-Using Databases with Python – Michigan University
The certification issued Jul 2020 – Credential ID: DPBCL48SAPJJ (
8-Using Python to Access Web Data- Michigan University
The certification issued Jun 2020-Credential ID: CJG7HGTEGJ93
9-Managing Big Data with MySQL-Duke University
The certification Issued Jul 2020-CredentialID: QCYK3V6XEU7L
10-Google Cloud Platform Fundamentals: Core Infrastructure – Google
The certification Issued Jun 2020 – Credential ID: CPD5RFPXEBQ7
11-NLP: Twitter Sentiment Analysis- Guided project – Coursera
The certification or issued Oct 2020 -Credential ID: WWKLB3ZY8XGR (
12-Analyze Text Data with Yellowbrick- Guided project – Coursera
The certification or issued Oct 2020-Credential ID: QLQF5CF3NK2A
13-Mining Data to Extract and Visualize Insights in Python – Coursera
The certification or issued Sep 2020-Credential ID: 4ZMA6UH2KBEY
14-Mining Quality Prediction Using Machine & Deep Learning – Coursera
The certification or issued Sep 2020-Credential ID: F58W6YYS3QTW
15-Introduction to Topic Modeling for Business – Coursera
The certification or issued Oct 2020-Credential ID: PEJ3AYFRQQDH (
16-Perform Sentiment Analysis with Scikit-learn – Coursera
The certification or issued Oct 2020-Credential ID: XSBMJXGFRLGG
17-Sentiment Analysis with Deep Learning using BERT – Coursera
The certification or issued Oct 2020-Credential ID: HVP9LSGXKJYQ
18-TensorFlow for AI: Computer Vision Basics – Coursera
The certification or issued Oct 2020-Credential ID: 8UY98MRD8T4L
19-Visualizing Citibike Trips with Tableau – Coursera
The certification or issued Oct 2020-Credential ID: 8UG87Y496STP
Tomas Bata University – Czech Republic
My main responsibility as a postdoc is to work with a research group to develop a deep learning model to classify plant diseases.
KAGGLE (Part- time)
- Kaggle Kernel Expert: Highest rank 316 out of 161724 global users, created 15 kernels with 1 Gold medal and 4 Bronze medals with a total of nearly 276 upvotes and 1186 forks.
- Data Scientist Competitor:
- Top 9% (Solo Bronze Medal) in Riiid Answer Correctness Prediction Competition.
- Top 20% in Cassava Leaf Disease Classification Prediction Competition.
Data Science & Machine Learning
- Fluency in Python with working knowledge of machine learning & Statistical libraries.
- Good experience in data exploratory, data visualizations, feature selection, data analysis using different techniques in Python.
- Strong experience working with different data types and different formats such as CSV, JSON, and XML
- Good experience in Data preparation, which includes cleaning and transforming raw data prior to processing and analysis.
- Strong Knowledge machine learning library Scikit-Learn, NumPy, Pandas, Seaborn, and OpenCV.
- Experience in processing real data and building ML pipelines end to end.
- Solid understanding and use of Machine Learning techniques and algorithms like Random forest, Gradient boosting, CatBoost, Light GBM, XGBoost to predict the outcomes.
- Strong experience working with and deep learning frameworks: Tensorflow, Keras, and PyTorch.
- Experience in build and training CNN, U-Net, Mask R-CNN, and Faster R-CNN models using deep learning framework such as Keras and Pytorch.
- Experience with image recognition, classification, and segmentation using computer vision techniques.
- Good understanding of model validation processes and optimizations.
- Experience applying machine learning techniques to NLP problems.
- Educational and professional experience in applying Machine Learning and Data Mining techniques to real problems with copious amounts of data.
- 18 years of professional experience in software design, development, debugging, deployment, documentation and testing of Client–Server and Web based Applications.
- Well-versed in business process modeling, business improvement, business system analysis, and developing responsive websites using C#, ASP.NET, MVC, JQuery, and SQL skills.
- Designing and developing reports by SAP Crystal Reports.
- Extensive Knowledge with .NET Framework (All versions).
- Highly experienced with SQL Server and MySQL.
- Extensive Knowledge in Visual Studio (All versions).
- Python, Java, C, C++, and C#.
- SQL Server and MySQL.
- Crystal Report.
- Jupyter, Tensorflow, Keras, and PyTorch.
Award Sheikh Salem Al-Ali Al Sabah Informatics. (2017)
- Project name: Mubader E-learning project – http://sch-kw.com/.
2-Award of Kuwait Foundation for the Advancement of Sciences – (2015)
- Award name: Kuwait E-Content Award.
- Project name: capital educational website – https://www.capital.edu.com/.
- Ali, A. A., Vařacha, P., Krayem, S., Žáček, P., & Urbanek, A. (2018). Distributed data mining systems: techniques, approaches and algorithms. In MATEC Web of Conferences. EDP Sciences.
- Ali, A. A., Vařacha, P., Krayem, S., Jašek, R., Žáček, P., & Chramcov, B. (2018). Modeling Of Distributed File System In Big Data Storage By Event-B. In MATEC Web of Conferences. EDP Sciences.
- Ali, A. A., Jasek, R., Krayem, S., Chramcov, B., & Zacek, P. (2018, April). Improved Adaptive Fault Tolerance Model for Increasing Reliability in Cloud Computing Using Event-B. In Computer Science On-line Conference (pp. 246-258). Springer, Cham.
- Capek, P., Jasek, R., Kral, E., Ali, A. A., & Senkerik, R. (2018, December). Cross Platform Configurable ERP Framework. In 2018 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1456-1457). IEEE.
- Krayem-Ivo, A. A. A. S., Alarsan-Mohammad, L. N. C. M., & Awwama, K. E. Solving Np-Complete Problem Using Formal Method Event-B.
- Ali, A. A., Krayem, S., Chramcov, B., & Kadi, M. F. (2018). Self-Stabilizing Fault Tolerance Distributed Cyber Physical Systems. Annals of DAAAM & Proceedings, 29.
- Ali, A. A., Jasek, R., Krayem, S., & Zacek, P. (2017, April). Proving the Effectiveness of Negotiation Protocols KQML in Multi-agent Systems Using Event-B. In Computer Science On-line Conference (pp. 397-406). Springer, Cham.