Hazem Mohammed, Developer in 6th of October City, Giza Governorate, Egypt
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Hazem Mohammed

Verified Expert  in Engineering

Data Scientist and Developer

6th of October City, Giza Governorate, Egypt

Toptal member since January 17, 2024

Expertise
Data ScienceData VisualizationMachine LearningData AnalysisComputer VisionWindows DevelopmentVisual Studio DevelopmentPythonNumPy
Bio

Hazem is a versatile data scientist and machine learning engineer who unravels complex patterns and extracts valuable insights from vast datasets. With a passion for turning raw information into actionable solutions, his expertise lies in developing innovative algorithms and predictive models. Hazem's work empowers businesses to make informed decisions and optimize their utility operations through excellent attention to detail and commitment to excellence.

Portfolio

Vodafone Intelligent Solutions (VOIS)
Machine Learning, Deep Learning, Model Development, Model Deployment...
Freelance
Data Analysis, Machine Learning, Deep Learning, Python, TensorFlow...
Integrated Technology Group (ITG)
Python 3, Machine Learning, Deep Learning, Large Language Models (LLMs)...

Experience

  • Python - 4 years
  • Artificial Intelligence (AI) - 3 years
  • Deep Learning - 2 years
  • Machine Learning - 2 years
  • Computer Vision - 2 years
  • Generative Artificial Intelligence (GenAI) - 1 year
  • Large Language Models (LLMs) - 1 year
  • Google Cloud Platform (GCP) - 1 year

Availability

Full-time

Preferred Environment

Windows, Visual Studio Code (VS Code), PyCharm, Jupyter Notebook, SQL Server 2017, TensorFlow, Google Cloud Platform (GCP), Vertex AI, Python 3

The most amazing...

...milestone I've accomplished is earning a job success score of 100% at an online freelance agency.

Work Experience

Machine Learning Engineer

2024 - PRESENT
Vodafone Intelligent Solutions (VOIS)
  • Designed and implemented scalable machine learning pipelines leveraging Vertex AI and GCP services.
  • Optimized model deployment and monitoring for efficient and reliable production workflows.
  • Automated workflows with CI/CD pipelines, Kubeflow pipelines, and MLOps best practices.
  • Automated the end-to-end ML lifecycle, including data ingestion, model training, evaluation, and deployment.
  • Developed clean and preprocessing components to turn raw data into usable formats for the production environment.
  • Built feature engineering components to improve model performance.
Technologies: Machine Learning, Deep Learning, Model Development, Model Deployment, Google Cloud Platform (GCP), Vertex AI, Google BigQuery, Machine Learning Operations (MLOps), Docker, Kubeflow, Large Language Models (LLMs), Chatbots, Retrieval-augmented Generation (RAG), Generative Artificial Intelligence (GenAI), Artificial Intelligence (AI), FastAPI, Amazon Web Services (AWS), APIs, Vector Search, Text Classification, Python 3, AI Model Training, BERT, Data Pipelines, Data Preparation, Language Models, PyTorch, Optical Character Recognition (OCR)

Data Scientist | Machine Learning Engineer

2022 - PRESENT
Freelance
  • Gained a 100% job success score for all my contracts.
  • Developed a one-dimensional CNN Grad-CAM computation for time-series data with explanatory charts.
  • Created a real-time face recognition system on custom data and deployed it using Raspberry BI and TensorFlow Lite.
  • Designed and implemented recommendation algorithms to personalize user experiences and improve customer engagement.
  • Architected computer vision algorithms for object detection, image segmentation, and model interpretability and applied them to real-world problems.
  • Analyzed large datasets, developed statistical models, machine learning algorithms, and data visualization techniques, and extracted insights to drive data-driven decision-making and solve business problems.
Technologies: Data Analysis, Machine Learning, Deep Learning, Python, TensorFlow, Scikit-learn, NumPy, Pandas, StatsModels, OpenCV, Scikit-image, Computer Vision, Natural Language Toolkit (NLTK), SQL, TensorFlow Lite, Functional Programming, Deep Neural Networks (DNNs), Object-oriented Programming (OOP), Natural Language Processing (NLP), Matplotlib, Seaborn, Exploratory Data Analysis, Time Series Analysis, ARIMAX, Forecasting, Data Wrangling, Data Visualization, Convolutional Neural Networks (CNNs), Object Detection, Image Segmentation, Transfer Learning, Model Interpretability, Saliency Maps, Class Activation Maps (CAMs), MobileNet, Residual Neural Networks (ResNets), You Only Look Once (YOLO), Visual Studio Code (VS Code), PyCharm, Jupyter Notebook, Data Structures, Windows, Recurrent Neural Networks (RNNs), Sequence Models, Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), Random Forests, Decision Trees, Support Vector Machines (SVM), Linear Regression, Ridge Regression, Lasso Regression, Logistic Regression, XGBoost, Ensemble Methods, Gradient Boosting, Regression Modeling, Classification, Excel 365, Optimization Algorithms, Artificial Neural Networks (ANN), Analytics, Amazon Web Services (AWS), Data Science, Keras, Artificial Intelligence (AI), Python 3, AI Model Training, Data Preparation, Gradient Descent, Adam Optimization Algorithm

Machine Learning Engineer

2024 - 2024
Integrated Technology Group (ITG)
  • Developed AI solutions for e-learning systems to improve the learning experience of students.
  • Offered customized schedules for students based on their progress and performance.
  • Developed and integrated a chatbot to help students tackle complex lessons and have an interactive learning experience.
Technologies: Python 3, Machine Learning, Deep Learning, Large Language Models (LLMs), Retrieval-augmented Generation (RAG), Chatbots, Classification, Text Classification, Vector Search, Text Processing, Regression, Model Deployment, APIs, Generative Artificial Intelligence (GenAI), Artificial Intelligence (AI), FastAPI, Docker, AI Model Training, BERT, Data Preparation, Gradient Descent, Adam Optimization Algorithm, Language Models, PyTorch

Artificial Intelligence Intern

2021 - 2021
Samsung Innovation Campus (SIC)
  • Learned about Probability theory, statistical inference, calculus, and linear algebra.
  • Developed expertise in supervised and unsupervised machine learning algorithms, enabling me to develop accurate models for prediction and classification tasks.
  • Used image processing techniques to preprocess and enhance visual data for further analysis effectively.
  • Understood the architecture and calculations involved in neural networks, allowing me to design and optimize deep learning models.
  • Leveraged RNNs and sequence models to effectively analyze and forecast time-series and NLP applications.
Technologies: Probability Theory, Statistics, Linear Algebra, Hypothesis Testing, A/B Testing, Machine Learning, Unsupervised Learning, Supervised Learning, Calculus, Deep Learning, Image Processing, Convolutional Neural Networks (CNNs), Sequence Models, Recurrent Neural Networks (RNNs), Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), Exploratory Data Analysis, Clustering, Scikit-learn, K-means Clustering, Hierarchical Clustering, Random Forests, Decision Trees, Support Vector Machines (SVM), Linear Regression, Ridge Regression, Lasso Regression, Logistic Regression, XGBoost, Gradient Boosting, Ensemble Methods, Scikit-image, OpenCV, Text Processing, Natural Language Toolkit (NLTK), Data Visualization, Matplotlib, Seaborn, Pandas, NumPy, TensorFlow, Regression Modeling, Classification, Windows, Visual Studio Code (VS Code), Jupyter Notebook, Deep Neural Networks (DNNs), Object-oriented Programming (OOP), Data Analysis, Optimization Algorithms, Artificial Neural Networks (ANN), Data Science, Keras, Artificial Intelligence (AI), Python 3, AI Model Training, Data Preparation, Gradient Descent, Adam Optimization Algorithm

Business Intelligence Developer Intern

2021 - 2021
Information Technology Institute (ITI)
  • Utilized my expertise in databases, SQL programming, and data mining, I optimized database queries and extracted valuable information.
  • Became proficient in Microsoft Power BI and Tableau. I created visually engaging dashboards that enhanced data comprehension.
  • Designed, implemented, and maintained ETL processes, procedures, and policies to support business analytics and reporting.
Technologies: SQL, SQL Server 2017, SQL Server BI, Microsoft Power BI, Tableau, Databases, SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSRS), XML, Data Mining, Windows, Visual Studio Code (VS Code), Jupyter Notebook, Data Analysis, Analytics, Dashboards, Reporting, Looker

Experience

Bird Object Localization Model

https://github.com/HazemMohammed100/Birds-Object-Localization
Birds are an integral part of our ecosystem, and their study and conservation require accurate identification and localization. Traditional methods of bird detection often rely on manual observation or limited datasets, making them time-consuming and less efficient. This project addresses these challenges by leveraging the power of deep learning and computer vision techniques to create a bird object localization model aimed at detecting and localizing birds in images.

The bird object localization model utilizes a state-of-the-art deep learning algorithm and is fine-tuned on a custom dataset to automatically identify and locate birds in digital images. The model's architecture combines convolutional neural networks with advanced localization techniques, enabling it to outline the boundaries of birds in images with great precision.

Ford GoBike System Data Analysis

https://github.com/HazemMohammed100/Ford-GoBike-System-Data-Analysis/tree/main
This project involved performing data analysis on data regarding 183,000 rides made in a bike-sharing system covering the greater San Francisco Bay area. The data consisted of 16 features describing each ride in terms of duration in seconds, date, customer type, and gender, as well as other additional variables.

CO2 Level Analysis and Forecasting

https://github.com/HazemMohammed100/CO2-Levels-Analysis-and-Forecasting
This analytical and forecasting project studied CO2 levels from March 1958 to September 2018 by estimating parts per million (PPM). This unit calculates the concentration of a substance in a solution or gas and offers a way to express small quantities of a specific substance within a more extensive mixture. In air quality, PPM represents the number of molecules of CO2 per million air molecules.

The increasing awareness regarding indoor air quality made individuals prone to use CO2 detectors to monitor their airflows. Users can mitigate airborne illnesses and live healthier lives by measuring indoor air quality and CO2 PPM levels.

Education

2017 - 2022

Bachelor's Degree in Computer Science and Engineering

Faculty of Electronic Engineering, Menoufia University - Menoufia, Egypt

Certifications

OCTOBER 2024 - PRESENT

Professional Machine Learning Engineer

Google Cloud

AUGUST 2024 - PRESENT

Prepare Data for ML APIs on Google Cloud Skill Badge

Google Cloud

AUGUST 2024 - PRESENT

Build and Deploy Machine Learning Solutions on Vertex AI Skill Badge

Google Cloud

FEBRUARY 2024 - PRESENT

Deep Learning

Coursera

FEBRUARY 2024 - PRESENT

Structuring Machine Learning Projects

DeepLearning.AI via Coursera

NOVEMBER 2023 - PRESENT

Generative Deep Learning with TensorFlow

Coursera

AUGUST 2023 - PRESENT

Natural Language Processing with Sequence Models

Coursera

JULY 2022 - PRESENT

Convolutional Neural Networks

Coursera

APRIL 2022 - PRESENT

Advanced Computer Vision with TensorFlow

Coursera

OCTOBER 2021 - PRESENT

Advanced Data Analysis

Udacity

Skills

Libraries/APIs

TensorFlow, Scikit-learn, NumPy, Pandas, Matplotlib, XGBoost, Keras, OpenCV, Natural Language Toolkit (NLTK), PyTorch

Tools

PyCharm, Seaborn, Microsoft Power BI, Scikit-image, ARIMAX, Named-entity Recognition (NER), You Only Look Once (YOLO), Tableau, BigQuery, StatsModels, SQL Server BI, Looker, AutoML, Google Cloud Dataproc

Languages

Python, SQL, C++, Java, XML, Python 3

Paradigms

Object-oriented Programming (OOP), Functional Programming, Siamese Neural Networks, Compiler Design

Platforms

Windows, Visual Studio Code (VS Code), Jupyter Notebook, Docker, Google Cloud Platform (GCP), Vertex AI, Kubeflow, Amazon Web Services (AWS)

Storage

SQL Server 2017, Databases, Google Cloud Storage, SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSRS), Data Pipelines

Frameworks

TensorFlow Lite

Other

Data Structures, Machine Learning, Deep Neural Networks (DNNs), Data Analysis, Computer Vision, Exploratory Data Analysis, Data Wrangling, Data Visualization, Convolutional Neural Networks (CNNs), Transfer Learning, MobileNet, Residual Neural Networks (ResNets), Computer Science, Supervised Learning, Calculus, Random Forests, Decision Trees, Support Vector Machines (SVM), Linear Regression, Ridge Regression, Lasso Regression, Logistic Regression, Gradient Boosting, Ensemble Methods, Regression Modeling, Classification, Artificial Neural Networks (ANN), Data Science, Software Engineering, Deep Learning, Time Series Analysis, Forecasting, A/B Testing, Hypothesis Testing, Object Detection, Image Segmentation, Model Interpretability, Neural Style Transfer (NST), Sequence Models, Recurrent Neural Networks (RNNs), Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), Sentiment Analysis, Probability Theory, Unsupervised Learning, Image Processing, Clustering, K-means Clustering, Hierarchical Clustering, Excel 365, Optimization Algorithms, Adam Optimization Algorithm, Gradient Descent, Language Models, Analytics, Dashboards, Reporting, Large Language Models (LLMs), Retrieval-augmented Generation (RAG), Chatbots, Vector Search, Regression, Model Deployment, APIs, Model Development, Google BigQuery, Machine Learning Operations (MLOps), Data Processing, ML APIs, Google Cloud Build, AI Model Training, Generative Artificial Intelligence (GenAI), Artificial Intelligence (AI), FastAPI, Optical Character Recognition (OCR), Expert Systems, Operating Systems, Computer Networking, Natural Language Processing (NLP), Saliency Maps, Class Activation Maps (CAMs), Word Embedding, Autoencoders, Variational Autoencoders, Generative Adversarial Networks (GANs), Data Mining, Statistics, Linear Algebra, Text Processing, Transformer Models, Text Classification, Scalability, BERT, Google Cloud Dataflow, Data Preparation

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