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$15 USD / hour
Flag of YEMEN
sanaa, yemen
$15 USD / hour
It's currently 2:02 AM here
Joined December 1, 2020
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Abdulaziz Ahmed H.

@abdulaziz602842

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$15 USD / hour
Flag of YEMEN
sanaa, yemen
$15 USD / hour
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Mechatronics & Robotics Engineer| AI & Python Dev

#_Experienced Mechatronics Engineer with a strong passion for Robotics and AI. #_Proficient in designing and developing innovative mechatronic and robotic systems, integrating mechanical, electrical, and software components, using common modeling and simulation programs: ---> Mechanical Systems : Solidworks ---> Control Systems: Matlab, Simulink, Simscape ---> Robotics : RobotStudio-ABB, V-Rep, CoppilaSim #_Skilled in programming languages such as Python, C++, and Matlab, and experienced in using frameworks and tools for AI applications. #_Deep understanding of control systems, sensor integration, and machine learning algorithms. #_Committed to creating intelligent and efficient solutions that push the boundaries of robotics technology. #_Strong problem-solving abilities and ability to collaborate effectively in multidisciplinary teams. #_Seeking challenging projects in the field of robotics and AI to apply expertise and contribute to cutting-edge advancements.
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Portfolio

11612206
1#_In this project I have accomplished an Image classifier for Iris image breeds to used as an image classifier in a smart phone app. I used deep learning model to be trained on hundreds of thousands of images as part of the overall application architecture. 2#_ It could be considered as integrated AI algorithm in a phone app that tells you the name of the flower your camera is looking at. 3#_ The deep learning model architecture was accomplished using Pytorch with some sub-modules for data preprocessing and models architecture. 4#_The dataset used for training the model consists of of 102 flower categories, you can see a few images uploaded below. 5#_The project is broken down into multiple steps: a* Loading and preprocessing the image dataset. b* Train the image classifier on the dataset. c* Use the trained classifier to predict image content. 6#_On the complete architecture of this project I got an application that can be trained on any set of labeled images.
Building Image Classifier with Python & Pytorch
1#_In this project I have accomplished an Image classifier for Iris image breeds to used as an image classifier in a smart phone app. I used deep learning model to be trained on hundreds of thousands of images as part of the overall application architecture. 2#_ It could be considered as integrated AI algorithm in a phone app that tells you the name of the flower your camera is looking at. 3#_ The deep learning model architecture was accomplished using Pytorch with some sub-modules for data preprocessing and models architecture. 4#_The dataset used for training the model consists of of 102 flower categories, you can see a few images uploaded below. 5#_The project is broken down into multiple steps: a* Loading and preprocessing the image dataset. b* Train the image classifier on the dataset. c* Use the trained classifier to predict image content. 6#_On the complete architecture of this project I got an application that can be trained on any set of labeled images.
Building Image Classifier with Python & Pytorch
1#_In this project I have accomplished an Image classifier for Iris image breeds to used as an image classifier in a smart phone app. I used deep learning model to be trained on hundreds of thousands of images as part of the overall application architecture. 2#_ It could be considered as integrated AI algorithm in a phone app that tells you the name of the flower your camera is looking at. 3#_ The deep learning model architecture was accomplished using Pytorch with some sub-modules for data preprocessing and models architecture. 4#_The dataset used for training the model consists of of 102 flower categories, you can see a few images uploaded below. 5#_The project is broken down into multiple steps: a* Loading and preprocessing the image dataset. b* Train the image classifier on the dataset. c* Use the trained classifier to predict image content. 6#_On the complete architecture of this project I got an application that can be trained on any set of labeled images.
Building Image Classifier with Python & Pytorch
11723057
In this Data scraping project, I have accomplished web scarping with Python using: - BeautifulSoup - re (regular expression) - urllib - NumPy & Pandas - Matplotlib & Seaborn I have made web scraping of a 10K race that took place in Hillsboro, OR on June 2017, by requesting the url of the webpage, "http://www.hubertiming.com/results/2017GPTR10K" and then extracting the web data and html content into a Pandas data frame. Then making suited manipulation on the data frame so that being suited for being analyzed and visualized using Matplotlib and Seaborn for data visualizations.
Web Data Scraping, Visualization & Analysis with Python
In this Data scraping project, I have accomplished web scarping with Python using: - BeautifulSoup - re (regular expression) - urllib - NumPy & Pandas - Matplotlib & Seaborn I have made web scraping of a 10K race that took place in Hillsboro, OR on June 2017, by requesting the url of the webpage, "http://www.hubertiming.com/results/2017GPTR10K" and then extracting the web data and html content into a Pandas data frame. Then making suited manipulation on the data frame so that being suited for being analyzed and visualized using Matplotlib and Seaborn for data visualizations.
Web Data Scraping, Visualization & Analysis with Python
In this Data scraping project, I have accomplished web scarping with Python using: - BeautifulSoup - re (regular expression) - urllib - NumPy & Pandas - Matplotlib & Seaborn I have made web scraping of a 10K race that took place in Hillsboro, OR on June 2017, by requesting the url of the webpage, "http://www.hubertiming.com/results/2017GPTR10K" and then extracting the web data and html content into a Pandas data frame. Then making suited manipulation on the data frame so that being suited for being analyzed and visualized using Matplotlib and Seaborn for data visualizations.
Web Data Scraping, Visualization & Analysis with Python
In this Data scraping project, I have accomplished web scarping with Python using: - BeautifulSoup - re (regular expression) - urllib - NumPy & Pandas - Matplotlib & Seaborn I have made web scraping of a 10K race that took place in Hillsboro, OR on June 2017, by requesting the url of the webpage, "http://www.hubertiming.com/results/2017GPTR10K" and then extracting the web data and html content into a Pandas data frame. Then making suited manipulation on the data frame so that being suited for being analyzed and visualized using Matplotlib and Seaborn for data visualizations.
Web Data Scraping, Visualization & Analysis with Python
In this Data scraping project, I have accomplished web scarping with Python using: - BeautifulSoup - re (regular expression) - urllib - NumPy & Pandas - Matplotlib & Seaborn I have made web scraping of a 10K race that took place in Hillsboro, OR on June 2017, by requesting the url of the webpage, "http://www.hubertiming.com/results/2017GPTR10K" and then extracting the web data and html content into a Pandas data frame. Then making suited manipulation on the data frame so that being suited for being analyzed and visualized using Matplotlib and Seaborn for data visualizations.
Web Data Scraping, Visualization & Analysis with Python
11620520
At the core of this project lies the challenge of examining the fluctuating durations of movies available on the platform. Leveraging my advanced Python skills, I embarked on a comprehensive analysis of a CSV file containing Netflix data, meticulously crafted by my friend. Together, we hypothesized that the average duration of movies has been experiencing a decline over time. Embark on this journey with me as we dissect the Netflix data and unveil the secrets behind the evolution of movie durations. Through this portfolio item, you will witness my expertise in action, highlighting my skills in exploratory data analysis, Python programming, statistical analysis, and data visualization with special workspace and Jupyter Notebook playground.
Python Analysis :Unraveling Trend Netflix Movies Durations
At the core of this project lies the challenge of examining the fluctuating durations of movies available on the platform. Leveraging my advanced Python skills, I embarked on a comprehensive analysis of a CSV file containing Netflix data, meticulously crafted by my friend. Together, we hypothesized that the average duration of movies has been experiencing a decline over time. Embark on this journey with me as we dissect the Netflix data and unveil the secrets behind the evolution of movie durations. Through this portfolio item, you will witness my expertise in action, highlighting my skills in exploratory data analysis, Python programming, statistical analysis, and data visualization with special workspace and Jupyter Notebook playground.
Python Analysis :Unraveling Trend Netflix Movies Durations
11694109
#_The main objective for this project is to automatically generate keywords for a search engine marketing campaign using Python. #_Such this technique is dedicated for a digital marketing agency, which is approached by a massive online retailer of furniture, asking with creating a prototype set of keywords for search campaigns for their sofas section. #_The most important task in structuring a search engine marketing account is mapping the right keywords to the right ads and making sure they send users to the right landing pages. Having figured that out is a big part of the account setup and makes the life of the account manager much easier with such this Keywords generation. #_With my Python skills, I have efficiently created these keywords using Python libraries and tools such NumPy, Pandas, and Matplotlib and Seaborn!
Generating Keywords for Google Ads with Python
11612206
1#_In this project I have accomplished an Image classifier for Iris image breeds to used as an image classifier in a smart phone app. I used deep learning model to be trained on hundreds of thousands of images as part of the overall application architecture. 2#_ It could be considered as integrated AI algorithm in a phone app that tells you the name of the flower your camera is looking at. 3#_ The deep learning model architecture was accomplished using Pytorch with some sub-modules for data preprocessing and models architecture. 4#_The dataset used for training the model consists of of 102 flower categories, you can see a few images uploaded below. 5#_The project is broken down into multiple steps: a* Loading and preprocessing the image dataset. b* Train the image classifier on the dataset. c* Use the trained classifier to predict image content. 6#_On the complete architecture of this project I got an application that can be trained on any set of labeled images.
Building Image Classifier with Python & Pytorch
1#_In this project I have accomplished an Image classifier for Iris image breeds to used as an image classifier in a smart phone app. I used deep learning model to be trained on hundreds of thousands of images as part of the overall application architecture. 2#_ It could be considered as integrated AI algorithm in a phone app that tells you the name of the flower your camera is looking at. 3#_ The deep learning model architecture was accomplished using Pytorch with some sub-modules for data preprocessing and models architecture. 4#_The dataset used for training the model consists of of 102 flower categories, you can see a few images uploaded below. 5#_The project is broken down into multiple steps: a* Loading and preprocessing the image dataset. b* Train the image classifier on the dataset. c* Use the trained classifier to predict image content. 6#_On the complete architecture of this project I got an application that can be trained on any set of labeled images.
Building Image Classifier with Python & Pytorch
1#_In this project I have accomplished an Image classifier for Iris image breeds to used as an image classifier in a smart phone app. I used deep learning model to be trained on hundreds of thousands of images as part of the overall application architecture. 2#_ It could be considered as integrated AI algorithm in a phone app that tells you the name of the flower your camera is looking at. 3#_ The deep learning model architecture was accomplished using Pytorch with some sub-modules for data preprocessing and models architecture. 4#_The dataset used for training the model consists of of 102 flower categories, you can see a few images uploaded below. 5#_The project is broken down into multiple steps: a* Loading and preprocessing the image dataset. b* Train the image classifier on the dataset. c* Use the trained classifier to predict image content. 6#_On the complete architecture of this project I got an application that can be trained on any set of labeled images.
Building Image Classifier with Python & Pytorch
11723057
In this Data scraping project, I have accomplished web scarping with Python using: - BeautifulSoup - re (regular expression) - urllib - NumPy & Pandas - Matplotlib & Seaborn I have made web scraping of a 10K race that took place in Hillsboro, OR on June 2017, by requesting the url of the webpage, "http://www.hubertiming.com/results/2017GPTR10K" and then extracting the web data and html content into a Pandas data frame. Then making suited manipulation on the data frame so that being suited for being analyzed and visualized using Matplotlib and Seaborn for data visualizations.
Web Data Scraping, Visualization & Analysis with Python
In this Data scraping project, I have accomplished web scarping with Python using: - BeautifulSoup - re (regular expression) - urllib - NumPy & Pandas - Matplotlib & Seaborn I have made web scraping of a 10K race that took place in Hillsboro, OR on June 2017, by requesting the url of the webpage, "http://www.hubertiming.com/results/2017GPTR10K" and then extracting the web data and html content into a Pandas data frame. Then making suited manipulation on the data frame so that being suited for being analyzed and visualized using Matplotlib and Seaborn for data visualizations.
Web Data Scraping, Visualization & Analysis with Python
In this Data scraping project, I have accomplished web scarping with Python using: - BeautifulSoup - re (regular expression) - urllib - NumPy & Pandas - Matplotlib & Seaborn I have made web scraping of a 10K race that took place in Hillsboro, OR on June 2017, by requesting the url of the webpage, "http://www.hubertiming.com/results/2017GPTR10K" and then extracting the web data and html content into a Pandas data frame. Then making suited manipulation on the data frame so that being suited for being analyzed and visualized using Matplotlib and Seaborn for data visualizations.
Web Data Scraping, Visualization & Analysis with Python
In this Data scraping project, I have accomplished web scarping with Python using: - BeautifulSoup - re (regular expression) - urllib - NumPy & Pandas - Matplotlib & Seaborn I have made web scraping of a 10K race that took place in Hillsboro, OR on June 2017, by requesting the url of the webpage, "http://www.hubertiming.com/results/2017GPTR10K" and then extracting the web data and html content into a Pandas data frame. Then making suited manipulation on the data frame so that being suited for being analyzed and visualized using Matplotlib and Seaborn for data visualizations.
Web Data Scraping, Visualization & Analysis with Python
In this Data scraping project, I have accomplished web scarping with Python using: - BeautifulSoup - re (regular expression) - urllib - NumPy & Pandas - Matplotlib & Seaborn I have made web scraping of a 10K race that took place in Hillsboro, OR on June 2017, by requesting the url of the webpage, "http://www.hubertiming.com/results/2017GPTR10K" and then extracting the web data and html content into a Pandas data frame. Then making suited manipulation on the data frame so that being suited for being analyzed and visualized using Matplotlib and Seaborn for data visualizations.
Web Data Scraping, Visualization & Analysis with Python
11620520
At the core of this project lies the challenge of examining the fluctuating durations of movies available on the platform. Leveraging my advanced Python skills, I embarked on a comprehensive analysis of a CSV file containing Netflix data, meticulously crafted by my friend. Together, we hypothesized that the average duration of movies has been experiencing a decline over time. Embark on this journey with me as we dissect the Netflix data and unveil the secrets behind the evolution of movie durations. Through this portfolio item, you will witness my expertise in action, highlighting my skills in exploratory data analysis, Python programming, statistical analysis, and data visualization with special workspace and Jupyter Notebook playground.
Python Analysis :Unraveling Trend Netflix Movies Durations
At the core of this project lies the challenge of examining the fluctuating durations of movies available on the platform. Leveraging my advanced Python skills, I embarked on a comprehensive analysis of a CSV file containing Netflix data, meticulously crafted by my friend. Together, we hypothesized that the average duration of movies has been experiencing a decline over time. Embark on this journey with me as we dissect the Netflix data and unveil the secrets behind the evolution of movie durations. Through this portfolio item, you will witness my expertise in action, highlighting my skills in exploratory data analysis, Python programming, statistical analysis, and data visualization with special workspace and Jupyter Notebook playground.
Python Analysis :Unraveling Trend Netflix Movies Durations
11694109
#_The main objective for this project is to automatically generate keywords for a search engine marketing campaign using Python. #_Such this technique is dedicated for a digital marketing agency, which is approached by a massive online retailer of furniture, asking with creating a prototype set of keywords for search campaigns for their sofas section. #_The most important task in structuring a search engine marketing account is mapping the right keywords to the right ads and making sure they send users to the right landing pages. Having figured that out is a big part of the account setup and makes the life of the account manager much easier with such this Keywords generation. #_With my Python skills, I have efficiently created these keywords using Python libraries and tools such NumPy, Pandas, and Matplotlib and Seaborn!
Generating Keywords for Google Ads with Python

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Experience

AWS Deepracer Reinforcement Learning Competitor

AWS (Amazon Web Services)
Jun 2023 - Present
Through out participating in the AWS Deepracer Student League, we train models with reward function and Reinforcement Learning techniques and submit them to the leaderboard. This could be considered as a Race against other students at the national, regional, and global levels every month. While training the models we can view a real-time simulation of the AWS DeepRacer car training around the race track and then develop an enhanced model for self-driving on tracks and avoiding the obstacles.

Education

Bachelor's degree in Mechatronics Engineering

Sana'a University, Yemen 2018 - 2023
(5 years)

Qualifications

Design Thinking and Product Development Principles

Udemy
2022
I have learned the Design Thinking strategies and approaches followed in almost big companies which are: 1_Empathize. 2_Define. 3_Ideate. 4_Prototype. 5_Test. In addition, I have got the concepts of Product Development which are: 1_Challenges & Characteristics. 2_Cost & Duration. I have experienced these principles with real-world case-study of Electronic Brakeforce Distribution System. Furthermore, I have learned the difference between Agile, Design Thinking, and Design Sprint.

AI Fundamentals

DataCamp
2023
I am excited to announce my completion of the AI Fundamental Certification on DataCamp, demonstrating my proficiency in AI Fundamentals, identifying use cases, explaining Generative AI, LLMs, ML principles, constructing prompts, and understanding ethical considerations. Through 20 hours of comprehensive training, I gained deep knowledge of AI applications and ethics. DataCamp's rigorous assessment methods, including a timed exam and diverse questions, confirmed my AI expertise with confidence.

AI programming with Python Nanodegree

Udacity
2023
In this Nanodegree certification I have gained strong qualifications and skills for: 1#_Linear Algebra Essentials/NumPy, matplotlib, Vector visualization, Systems of linear equations, Linear algebra, Matrix multiplication. 2#_Calculus Essentials/Limits of functions, Gradient descent, Backpropagation, Integrals, Derivatives, Implicit differentiation ,Chain rule application. 3#_Training neural networks, Backpropagation, PyTorch, AI algorithms in Python, Gradient descent.

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Top Skills

Python
Engineering
Microcontroller
Solidworks
PLC & SCADA

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