MOHAMMAD SAAMIR
Summary
Recent Computer Graduate with a strong foundation in software development, programming languages and problem solving. Proficient in core programming concepts and eager to apply academic knowledge to real-world projects. Experienced in working on diverse programming assignments and collaborative projects, with a keen interest in continuing to develop technical skills and contribute to innovative solutions in a dynamic team environment.
Overview
1
1
Certification
Education
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Skills
- Hands on Process Mining and developing automation solutions with Celonis
- Creating Data Jobs and Transformations for loading data from different databases/Source systems into one or many databases
- Data Engineering tasks in Celonis such as Data Extraction, Transformation and Modelling
- Hands on Celonis analytics tasks such as Dashboard Creation, and building Business Analysis
- Worked in writing SQL transformation scripts and validating the data to transform the data in Celonis
- Expertise in Event log, Action Engine, Transformation Center and Studio (Action Flows,skills and Views)
- Hands on developing Python and Machine learning scripts in Celonis
- Involved in end-to-end project cycle including Requirements gathering, Solution designing, Coding, Testing, Debugging, Documentation, and Implementation using Celonis
- Worked on Automating Processes, Ticketing, approvals, and Email automation
- Through understanding of the SDLC with proficiency in mapping client requirements & application design Using SQL, Python, Numpy, and Pandas Proficient in Machine Learning
PROJECTS
A Study on Food Value Estimation From Images: Taxonomies, Datasets and Techniques
- Algorithms used- Convolutional Neural Networks, Object Detection Algorithms
- Tools used- SQl, Python , Flask
- Conducted extensive research for a " A Study on Food Value Estimation From Images: Taxonomies, Datasets and Techniques " enhancing food recognition algorithms and boosting classification accuracy by 25%, significantly impacting computer vision research.
Predicting The Facial Expression Recognition Using AI
- Algorithms used- Convolutional Neural Networks, Recurrent Neural Networks
- Tools used- TensorFlow, Keras, Pytorch and OpenCV
- Implemented AI models using Convolutional Neural Networks and Recurrent Neural Networks, achieving 92% accuracy rate in predicting facial expression across diverse datasets.
Certification
Celonis Implementation Professional.
- Data Transformation using Python
- Automated Processes using Celonis
- Created Dashboards
- Tools used- Celonis
Coincent Industry Training.
- Worked on Applications Using HTML5, CSS and JavaScript
Hobbies
- Badminton
- Football
- Cricket
- Table Tennis
- Swimming
Timeline
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