class MostafaFakhry:
def __init__(self):
self.username = "nexxusMostaFA"
self.location = "Cairo, Egypt"
self.education = "Computer Science @ Cairo University"
self.current_focus = ["Artificial Intelligence", "Data Science", "Machine Learning"]
self.interests = ["Deep Learning", "Computer Vision", "NLP", "MLOps"]
def say_hi(self):
print("Thanks! Let's build something amazing together.")
def current_projects(self):
return [
"Football Analysis with YOLO & Computer Vision",
"Advanced ML Model Development",
"Data Science Competitions on Kaggle"
]
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Core Python Programming
- Variables, Data Types, and Operators
- Control Flow (if/else, loops, break/continue)
- Functions, Lambda, and Decorators
- Object-Oriented Programming (Classes, Inheritance, Polymorphism)
- Exception Handling and Error Management
- File Handling (read/write operations)
- Modules and Packages
- Regular Expressions
- Working with APIs
Status: ✅ Completed | Duration: 3 months
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NumPy
- Arrays and Matrix Operations
- Broadcasting and Vectorization
- Statistical Operations
- Linear Algebra Functions
Pandas
- DataFrames and Series
- Data Cleaning and Preprocessing
- Handling Missing Values
- Data Transformation and Aggregation
- Groupby Operations
- Merging and Joining DataFrames
- Time Series Analysis
Data Visualization
- Matplotlib (Line plots, Bar charts, Histograms, Scatter plots)
- Seaborn (Statistical visualizations, Heatmaps, Pair plots)
- Plotly (Interactive visualizations)
SQL
- Database Design and Normalization
- Joins and Subqueries
- Aggregate Functions and Window Functions
Status: ✅ Completed | Duration: 4 months
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Statistics & Probability
- Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)
- Probability Distributions (Normal, Binomial, Poisson)
- Confidence Intervals
- Correlation and Causation
Feature Engineering
- Feature Scaling (Normalization, Standardization)
- Encoding Categorical Variables (One-Hot, Label Encoding)
- Feature Selection Techniques
- Handling Imbalanced Data
Exploratory Data Analysis (EDA)
- Data Profiling
- Outlier Detection
- Distribution Analysis
- Correlation Analysis
Status: ✅ Completed | Duration: 3 months
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Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines (SVM)
- Naive Bayes
- K-Nearest Neighbors (KNN)
- Gradient Boosting (XGBoost, LightGBM, CatBoost)
Unsupervised Learning
- K-Means Clustering
Model Evaluation
- Cross-Validation Techniques
- Confusion Matrix, Precision, Recall, F1-Score
- Mean Squared Error, R-squared
Hyperparameter Tuning
- Grid Search
- Random Search
Model Deployment
- Saving and Loading Models (Pickle, Joblib)
- Building APIs with Flask and FastAPI
- Model Serving
Status: ✅ Completed | Duration: 5 months
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Neural Network Fundamentals
- Perceptrons and Activation Functions
- Forward and Backward Propagation
- Loss Functions and Optimizers
- Batch Normalization and Dropout
- Weight Initialization Techniques
Convolutional Neural Networks (CNN)
- Convolutional Layers, Pooling Layers
- CNN Architectures (LeNet, AlexNet, VGG, ResNet, Inception)
- Transfer Learning
- Image Classification
- Object Detection (YOLO, R-CNN, Fast R-CNN, Faster R-CNN)
- Image Segmentation (U-Net, Mask R-CNN)
Frameworks
- TensorFlow & Keras
- PyTorch
- Hugging Face Transformers
Status: 🔄 NLP, RNN, LSTM and GANs In Progress
AI-Powered Sports Analytics Platform
Advanced computer vision system for real-time football match analysis using YOLOv8, ByteTrack, and deep learning.
Key Features:
- Multi-object detection and tracking (players, referees, ball)
- Automatic team classification using K-Means clustering
- Real-time speed and distance calculations
- Camera movement compensation with optical flow
- Perspective transformation for accurate measurements
- RESTful API for video processing
Tech Stack: YOLOv8 ByteTrack OpenCV Flask scikit-learn NumPy
Active participant in machine learning focus on:
- Computer Vision challenges
- Tabular data competitions
- Natural Language Processing tasks
Profile: kaggle.com/mostafataha2127095
Committed to staying at the forefront of AI/ML through:
- Research paper implementation
- Online course completion (Coursera, fast.ai, deeplearning.ai)
- Technical conference attendance
- Open-source contributions
- Research Collaborations in AI and Machine Learning
- Open Source Contributions to impactful projects
- Freelance Projects in Data Science and ML Engineering
- Internship Opportunities in AI/ML domains
- Knowledge Sharing through technical writing and speaking
I'm always excited to discuss:
- Innovative AI/ML solutions
- Computer vision applications
- Data science best practices
- Collaboration opportunities
- Tech trends and research
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Phone +20 101 918 5390 |
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Twitter/X |