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$25 USD / tunti
Maan TUNISIA lippu
sousse, tunisia
$25 USD / tunti
Aika on tällä hetkellä 2:01 ip. täällä
Liittynyt heinäkuuta 31, 2022
0 Recommendations

Belaid S.

@SamehB

annual-level-three.svg 5,0 (3 arvostelua)
3,0
3,0
100%
100%
$25 USD / tunti
Maan TUNISIA lippu
sousse, tunisia
$25 USD / tunti
100 %
Suoritetut työt
100 %
Budjetin mukaisesti
100 %
Aikataulussa
33 %
Uudelleenpalkkausaste

Python | AI | Web Development

I am fully dedicated to making your visions and innovative ideas a reality. I strive to not only meet but surpass your expectations, ensuring the transformation of your concepts into impactful and successful outcomes. ✅Bachelor Degree in Computer Science ✅Engineering degree in embedded systems ✅3 + Years Experience in IT & AI developpement I provide: √ Responsive Web Applications that make use of advanced AI technologies √ API development & Integration (OpenAI Whisper, GPT3 ...) √ Generative and predictive AI models My areas of expertise include : • Python, Matlab, JavaScript (React), C++ • Data Science ( Data analysis, Data preparation, Data cleaning, Visualization ) • Artificial intelligence ( Machine learning, NLP, Computer vision, Model tuning, Pipelining ) • Deep Learning ( CNN, LSTM, Yolo, GAN, Transformers, LLM...) • OpenAI • Docker • SQL
Freelancer Python Developers Tunisia
Katso kaikki Python Developers paikassa Tunisia

Ota yhteyttä käyttäjään Belaid S. työhösi liittyen

Kirjaudu sisään keskustellaksesi yksityiskohdista chatissa.

Portfolio

11733938
In this project i used Yolov8, which is a robust computer vision model that was trained on a very specific dataset, with a number of algorithms to accurately detect a unique pattern in the stock market data, the model works perfectly on 6 different intervals with sound notification to alert the client for any updates.
Stock market Real time pattern detection
In this project i used Yolov8, which is a robust computer vision model that was trained on a very specific dataset, with a number of algorithms to accurately detect a unique pattern in the stock market data, the model works perfectly on 6 different intervals with sound notification to alert the client for any updates.
Stock market Real time pattern detection
In this project i used Yolov8, which is a robust computer vision model that was trained on a very specific dataset, with a number of algorithms to accurately detect a unique pattern in the stock market data, the model works perfectly on 6 different intervals with sound notification to alert the client for any updates.
Stock market Real time pattern detection
11669462
The client sought to utilize AI tools for the creation of oil paintings that highlight chinese culture . The goal was to generate unique, high-quality artwork efficiently, harnessing the capabilities of artificial intelligence and these are some examples.
High quality AI artistic paintings
The client sought to utilize AI tools for the creation of oil paintings that highlight chinese culture . The goal was to generate unique, high-quality artwork efficiently, harnessing the capabilities of artificial intelligence and these are some examples.
High quality AI artistic paintings
11667143
Brain tumor detection and segmentation from MRI images using CNN and U-net models. These deep neural networks are implemented with Keras functional API. The CNN model is used to detect whether a tumor is there or not. After 15 epochs of training, the calculated accuracy is about 99.6%. The U-net model is used to segment tumors in MRI images of the brain with an accuracy of 98%.
Brain tumor Detection with segmentation
Brain tumor detection and segmentation from MRI images using CNN and U-net models. These deep neural networks are implemented with Keras functional API. The CNN model is used to detect whether a tumor is there or not. After 15 epochs of training, the calculated accuracy is about 99.6%. The U-net model is used to segment tumors in MRI images of the brain with an accuracy of 98%.
Brain tumor Detection with segmentation
11664844
• Created an ECG classification and monitoring system for diagnosing and identifying 5 arrhythmia types in patients using CNN. • The model achieved a 99% accuracy in distinguishing between different cases. • Collaborated with a skilled team of engineers to design the required hardware for this project for full implementation.
ECG Classification system
• Created an ECG classification and monitoring system for diagnosing and identifying 5 arrhythmia types in patients using CNN. • The model achieved a 99% accuracy in distinguishing between different cases. • Collaborated with a skilled team of engineers to design the required hardware for this project for full implementation.
ECG Classification system
• Created an ECG classification and monitoring system for diagnosing and identifying 5 arrhythmia types in patients using CNN. • The model achieved a 99% accuracy in distinguishing between different cases. • Collaborated with a skilled team of engineers to design the required hardware for this project for full implementation.
ECG Classification system
• Created an ECG classification and monitoring system for diagnosing and identifying 5 arrhythmia types in patients using CNN. • The model achieved a 99% accuracy in distinguishing between different cases. • Collaborated with a skilled team of engineers to design the required hardware for this project for full implementation.
ECG Classification system
11733938
In this project i used Yolov8, which is a robust computer vision model that was trained on a very specific dataset, with a number of algorithms to accurately detect a unique pattern in the stock market data, the model works perfectly on 6 different intervals with sound notification to alert the client for any updates.
Stock market Real time pattern detection
In this project i used Yolov8, which is a robust computer vision model that was trained on a very specific dataset, with a number of algorithms to accurately detect a unique pattern in the stock market data, the model works perfectly on 6 different intervals with sound notification to alert the client for any updates.
Stock market Real time pattern detection
In this project i used Yolov8, which is a robust computer vision model that was trained on a very specific dataset, with a number of algorithms to accurately detect a unique pattern in the stock market data, the model works perfectly on 6 different intervals with sound notification to alert the client for any updates.
Stock market Real time pattern detection
11669462
The client sought to utilize AI tools for the creation of oil paintings that highlight chinese culture . The goal was to generate unique, high-quality artwork efficiently, harnessing the capabilities of artificial intelligence and these are some examples.
High quality AI artistic paintings
The client sought to utilize AI tools for the creation of oil paintings that highlight chinese culture . The goal was to generate unique, high-quality artwork efficiently, harnessing the capabilities of artificial intelligence and these are some examples.
High quality AI artistic paintings
11667143
Brain tumor detection and segmentation from MRI images using CNN and U-net models. These deep neural networks are implemented with Keras functional API. The CNN model is used to detect whether a tumor is there or not. After 15 epochs of training, the calculated accuracy is about 99.6%. The U-net model is used to segment tumors in MRI images of the brain with an accuracy of 98%.
Brain tumor Detection with segmentation
Brain tumor detection and segmentation from MRI images using CNN and U-net models. These deep neural networks are implemented with Keras functional API. The CNN model is used to detect whether a tumor is there or not. After 15 epochs of training, the calculated accuracy is about 99.6%. The U-net model is used to segment tumors in MRI images of the brain with an accuracy of 98%.
Brain tumor Detection with segmentation
11664844
• Created an ECG classification and monitoring system for diagnosing and identifying 5 arrhythmia types in patients using CNN. • The model achieved a 99% accuracy in distinguishing between different cases. • Collaborated with a skilled team of engineers to design the required hardware for this project for full implementation.
ECG Classification system
• Created an ECG classification and monitoring system for diagnosing and identifying 5 arrhythmia types in patients using CNN. • The model achieved a 99% accuracy in distinguishing between different cases. • Collaborated with a skilled team of engineers to design the required hardware for this project for full implementation.
ECG Classification system
• Created an ECG classification and monitoring system for diagnosing and identifying 5 arrhythmia types in patients using CNN. • The model achieved a 99% accuracy in distinguishing between different cases. • Collaborated with a skilled team of engineers to design the required hardware for this project for full implementation.
ECG Classification system
• Created an ECG classification and monitoring system for diagnosing and identifying 5 arrhythmia types in patients using CNN. • The model achieved a 99% accuracy in distinguishing between different cases. • Collaborated with a skilled team of engineers to design the required hardware for this project for full implementation.
ECG Classification system

Arvostelut

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Näytetään 1 - 3 / 3 arvostelua
Suodata arvosteluja:
5,0
$220,00 USD
Project for Belaid S. -- 2
Quality of the work wow Communication soo good hire Belaid S. again
C Programming
Python
Machine Learning (ML)
API
+1 enemmän
Käyttäjän avatar
Maan lippu GdevDataSceince
@GdevDataSceince
•
kuukausi sitten
5,0
$246,00 CAD
Stock Trend ML Wizard Needed
Quality of the work hire Belaid S. again
Python
Machine Learning (ML)
Deep Learning
Livestock
Predictive Analytics
Käyttäjän avatar
Maan lippu GdevDataSceince
@GdevDataSceince
•
2 kuukautta sitten
5,0
$40,00 USD
CNN LSTM Model for Accuracy
Quality of the work great.i am happy with you thanks. hire Belaid S. again
Python
Algorithm
Machine Learning (ML)
Neural Networks
Käyttäjän avatar
Maan lippu GdevDataSceince
@GdevDataSceince
•
3 kuukautta sitten

Kokemus

AI engineer

ETC
maalisk. 2023 - elok. 2023 (5 kuukautta, 1 )
• Developed an "Arabic-English Dialect Assistant" that can handle the translation of both spoken and written languages. • The models achieved a blue score of 48 and 42 respectively using highly advanced techniques and limited resources. • Championed over many hurdles and contributed to the company by making Customers conversations and data more secure. • The Project was also implemented on a raspberry pi 4 to serve as a real time translation tool.

MATLAB Expert in Neural Networks

Launchpad
helmik. 2023 - toukok. 2023 (3 kuukautta, 4 päivää)
• Helped the team set up an adaptive cruise control system for vehicles by simulating and testing 6 Different approaches in MATLAB. • Improved the responsiveness of the system to brusque changes in speed by 20%. • Contributed to the research effort in creating a fully automated Self driving car.

Koulutus

Engineering degree in Microelectronics and Embedded systems

Tunisia 2020 - 2023
(3 vuotta)

Bachelor's degree in Computer science

Université de Sousse, Tunisia 2017 - 2020
(3 vuotta)

Ota yhteyttä käyttäjään Belaid S. työhösi liittyen

Kirjaudu sisään keskustellaksesi yksityiskohdista chatissa.

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Parhaat taidot

Python 3
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Deep Learning 2
C Programming 1
API 1

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