About Me

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I am a highly motivated and skilled junior Data Scientist and Big Data Analyst with a strong background in statistics and mathematics combined with heavy training on Artificial Intelligence, Machine Learning , neural network and Deep learning. However, my data field was build on a strong background in SQL and Big data analysis certified from Google, IBM, Cisco and AWS. Also have different skills in different visualizations programs Tableau, power BI, Excel and also Matplotlib using python making use of data to drive decisions. Build predictive models to analyze massive volumes of data to make sense of it. Develop dashboards which are interactive and provide insight into what’s happening within your system.

Also as a data scientist can add a great part of regression and ML modeling to the data to extract more information beside normal analysis and interactive dashboards.

As a fresh graduate i had a python for data science internship in FBK laboratory, my graduation project was building 3 different deep learning models to predict CAD disease patient using MRI images of 63,000 in a precision medicine setting. which was combined with GUI webapp of flask to show and interact with the results also a denoising autoencoder to enhance the model’s accuracy.

I’ve been working with a Chinese company named Luxury items based in Vietnamese, as a machine learning specialist to develop it’s back-end machine learning system and implement it in their website from 2020 to 2022.

My previous work experience includes python programming, data analysis, and visualization. also, Experience using Pytorch , FastAi and TensorFlow for deep learning and computer vision. Experience using Python for machine learning, data analysis, data visualization, and web development. Experience using scikit-learn for machine learning model development. I look forward to hearing from you soon..

Samer Kharboush
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Samer Kharboush

Work Process

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Research and Plan

This is the initial stage of any data analysis and machine learning project. In this stage, you need to understand the problem that you are trying to solve and the data that you have available to solve that problem. You also need to research and understand what methods and algorithms are suitable for solving the problem, as well as what kind of data preprocessing and feature engineering is needed.

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Design and Develop

In this stage, you will design and implement the solution based on the research and plan from the previous stage. You will need to implement the preprocessing and feature engineering techniques, as well as selecting and training a suitable machine learning model. You will also need to evaluate the model’s performance and fine-tune it if necessary.

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Deploy and visualization

Once you have a working machine learning model, you will need to deploy it in a real-world setting. This could involve integrating the model into an application, dashboard, website, or any other platform. In this stage, you will also need to visualize the results of the model to provide meaningful insights to the users.

My Clients

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