¡Hola, soy Ahmed Abdelrhman!
Data Analyst /Data Scientist/ML Engineer en Freelancing Platforms
Data Analytics and Machine Learning professional with deep expertise in statistical modeling, predictive analysis, and pattern recognition.A top graduate of Udacity's Advanced NanoDegree. Certified in machine learning (MITx).Applied skills extensively through +10 practical data science projects including medical no-shows, A/B tests, soccer top 5 leagues, and demographic data analysis. Experienced in developing customized solutions leveraging tools like Python, SQL, Tableau, and machine learning libraries. Solid mathematics, with a lifelong passion for using data to solve real-world problems.
Proyectos
Data Analyst / Statistician
For this project, I will be working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product. My goal is to work through this notebook to help the company understand if they should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.
Data Analyst
Cleaning A Massive Amount Of Data For Over 25000 Match. High Level Of Data Wrangling An Extensive Analysis Of The Factors Affecting The Performance Of Leagues Has Been Performed Throughout The Report. Revealing Unusual Patterns From Complex Dataset. Neat Visualizations And Drawing Accurate Conclusions.
Machine Learning Engineer
To automatically analyze reviews, i will need to complete the following tasks: Implement and compare three types of linear classifiers: the perceptron algorithm, the average perceptron algorithm, and the Pegasos algorithm. Use your classifiers on the food review dataset, using some simple text features. Experiment with additional features and explore their impact on classifier performance.
Machine Learning /AI Engineer
The MNIST database contains binary images of handwritten digits commonly used to train image processing systems. The digits were collected from Census Bureau employees and high school students. The database contains 60,000 training digits and 10,000 testing digits, all of which have been size-normalized and centered in a fixed-size image of 28 × 28 pixels. Frame work used PYTorch for CNN and a simple self-made algorithm for feed forward neural network.
Machine Learning /AI Engineer
The MNIST database contains binary images of handwritten digits commonly used to train image processing systems. The digits were collected from Census Bureau employees and high school students. The database contains 60,000 training digits and 10,000 testing digits, all of which have been size-normalized and centered in a fixed-size image of 28 × 28 pixels. Many methods have been tested with this dataset and in this project to experiment with the task of classifying these images into the correct digit using some of the methods like linear and logistic regression, non-linear features, regularization, and kernel tricks to see how these methods can be used to solve a real-life problem.
-Collaborative-Filtering-via-Gaussian-Mixtures-
•https://github.com/Ahmed8aa/-Collaborative-Filtering-via-Gaussian-Mixtures-Machine Learning /AI Engineer
The task was to build a mixture model for collaborative filtering, Given a data matrix containing movie ratings made by users where the matrix is extracted from a much larger Netflix database. Any particular user has rated only a small fraction of the movies so the data matrix is only partially filled. The goal is to predict all the remaining entries of the matrix. The model assumes that each user's rating profile is a sample from a mixture model. Using the Expectation Maximization (EM) algorithm to estimate a mixture from a partially observed rating matrix.
Text-Based-Game--Reinforcement-Learning
•https://github.com/Ahmed8aa/Text-Based-Game--Reinforcement-Learning-Machine Learning /AI Engineer
In this project, we address the task of learning control policies for text-based games using reinforcement learning. In these games, all interactions between players and the virtual world are through text. The current world state is described by elaborate text, and the underlying state is not directly observable. Players read descriptions of the state and respond with natural language commands to take actions.
Idiomas
Inglés
Francés
Habilidades
Pandas
NumPy
Scikit-Learn
PyTorch
SQL
Data Analytics
Data Science
Deep Learning
Deep Learning Algorithms
Reinforcement Learning
NLP
SciPy
Python
Recommender Systems
Clustering
Convolutional Neural Networks
Feature Engineering
SVM
Random Forest
Decision Trees
Spreadsheet
Hypothesis Testing
Regression Models
matplotlib
Statistical Analysis
Statsmodels
Bayesian inference
Probability
A/B Testing (Split Testing)
Data Cleaning
Data Wrangling
Agile
Scrum
Waterfall
Tableau
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