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$10 USD / tunti
Maan PAKISTAN lippu
lahore, pakistan
$10 USD / tunti
Aika on tällä hetkellä 9:49 ip. täällä
Liittynyt kesäkuuta 27, 2022
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Muhammad Rizwan A.

@mrizwanakram786

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$10 USD / tunti
Maan PAKISTAN lippu
lahore, pakistan
$10 USD / tunti
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Uudelleenpalkkausaste

Data Scientist

Data Scientist with expertise in Computer Vision and Natural Language Processing (NLP): Proficient in developing and deploying advanced machine learning models for image and text analysis. Experienced in leveraging deep learning frameworks, such as TensorFlow and PyTorch, to build and optimize computer vision models for tasks like object detection, image classification, and semantic segmentation. Skilled in NLP techniques like sentiment analysis, named entity recognition, and language modeling using tools like NLTK and spaCy. Strong problem-solving and data analysis skills with a proven track record of delivering actionable insights and driving business value.
Freelancer Linux Developers Pakistan
Katso kaikki Linux Developers paikassa Pakistan

Ota yhteyttä käyttäjään Muhammad Rizwan A. työhösi liittyen

Kirjaudu sisään keskustellaksesi yksityiskohdista chatissa.

Portfolio

11042868
Captcha Recognition is a project focused on developing a deep learning model to accurately recognize and decipher CAPTCHA images. By leveraging advanced image processing techniques and deep neural networks, the system aims to overcome the challenges posed by distorted characters and successfully decode CAPTCHAs. The project utilizes the CAPTCHA Version 2 dataset, and through extensive training and optimization, it aims to achieve high accuracy in recognizing and interpreting CAPTCHA images. This project is an essential step in automating CAPTCHA solving and enhancing security measures against automated bots.
Captcha Recognition
11039735
The "Car Number Plate Detection and Recognition" project aims to develop an advanced system capable of automatically detecting and recognizing number plates on car images. This project leverages computer vision techniques and deep learning algorithms to achieve accurate and efficient detection and recognition of car number plates. The primary objective of this project is to build a robust system that can automatically locate and extract number plates from car images, followed by the recognition of the characters on the plates. By combining state-of-the-art computer vision algorithms and optical character recognition (OCR) techniques, this system provides a comprehensive solution for car number plate analysis.
Car Number plate Detection and Recognition
11039629
Title: Apple vs Banana Classification Model with MobileNet Description: The "Apple vs Banana Classification Model with MobileNet" project focuses on developing a machine learning model capable of accurately distinguishing between images of apples and bananas. This project utilizes the MobileNet architecture, known for its efficiency and effectiveness in image classification tasks. The primary objective of this project is to leverage the power of deep learning and transfer learning to train a robust model that can classify images of apples and bananas with high accuracy. The MobileNet architecture, with its lightweight design and excellent feature extraction capabilities, allows for efficient training and deployment on resource-constrained devices.
Apple vs Banana classification model with MobileNet architec
Title: Apple vs Banana Classification Model with MobileNet Description: The "Apple vs Banana Classification Model with MobileNet" project focuses on developing a machine learning model capable of accurately distinguishing between images of apples and bananas. This project utilizes the MobileNet architecture, known for its efficiency and effectiveness in image classification tasks. The primary objective of this project is to leverage the power of deep learning and transfer learning to train a robust model that can classify images of apples and bananas with high accuracy. The MobileNet architecture, with its lightweight design and excellent feature extraction capabilities, allows for efficient training and deployment on resource-constrained devices.
Apple vs Banana classification model with MobileNet architec
11039595
The "Object Detection with YOLOv8" project focuses on utilizing the YOLOv8 architecture for accurate and efficient object detection tasks. YOLOv8 (You Only Look Once version 8) is a state-of-the-art deep learning model known for its real-time object detection capabilities. This project aims to leverage YOLOv8 to detect and localize objects within images or video frames with high accuracy. The YOLOv8 algorithm uses a single neural network to process the entire image or frame, predicting bounding boxes and class probabilities for multiple objects simultaneously. This allows for efficient and real-time object detection, making it suitable for various applications, including video surveillance, autonomous vehicles, and image analysis.
Object Detection with YOLOv8
11039584
The "Time Series Forecasting" project focuses on analyzing and predicting future values in time-dependent data. Time series forecasting plays a vital role in various domains, allowing us to anticipate trends, patterns, and behavior exhibited by data over time. This project aims to develop robust and accurate models for forecasting future values based on historical data. By leveraging advanced time series analysis techniques and machine learning algorithms, we can extract valuable insights and make informed predictions in a wide range of applications. The repository contains datasets for training and evaluating the forecasting models. Additionally, it provides Jupyter notebooks that offer step-by-step implementations of different time series forecasting techniques. These notebooks include explanations, code snippets, and visualizations to help users understand and apply the methodologies effectively. The project also includes a directory
Time Series Forecasting
11042868
Captcha Recognition is a project focused on developing a deep learning model to accurately recognize and decipher CAPTCHA images. By leveraging advanced image processing techniques and deep neural networks, the system aims to overcome the challenges posed by distorted characters and successfully decode CAPTCHAs. The project utilizes the CAPTCHA Version 2 dataset, and through extensive training and optimization, it aims to achieve high accuracy in recognizing and interpreting CAPTCHA images. This project is an essential step in automating CAPTCHA solving and enhancing security measures against automated bots.
Captcha Recognition
11039735
The "Car Number Plate Detection and Recognition" project aims to develop an advanced system capable of automatically detecting and recognizing number plates on car images. This project leverages computer vision techniques and deep learning algorithms to achieve accurate and efficient detection and recognition of car number plates. The primary objective of this project is to build a robust system that can automatically locate and extract number plates from car images, followed by the recognition of the characters on the plates. By combining state-of-the-art computer vision algorithms and optical character recognition (OCR) techniques, this system provides a comprehensive solution for car number plate analysis.
Car Number plate Detection and Recognition
11039629
Title: Apple vs Banana Classification Model with MobileNet Description: The "Apple vs Banana Classification Model with MobileNet" project focuses on developing a machine learning model capable of accurately distinguishing between images of apples and bananas. This project utilizes the MobileNet architecture, known for its efficiency and effectiveness in image classification tasks. The primary objective of this project is to leverage the power of deep learning and transfer learning to train a robust model that can classify images of apples and bananas with high accuracy. The MobileNet architecture, with its lightweight design and excellent feature extraction capabilities, allows for efficient training and deployment on resource-constrained devices.
Apple vs Banana classification model with MobileNet architec
Title: Apple vs Banana Classification Model with MobileNet Description: The "Apple vs Banana Classification Model with MobileNet" project focuses on developing a machine learning model capable of accurately distinguishing between images of apples and bananas. This project utilizes the MobileNet architecture, known for its efficiency and effectiveness in image classification tasks. The primary objective of this project is to leverage the power of deep learning and transfer learning to train a robust model that can classify images of apples and bananas with high accuracy. The MobileNet architecture, with its lightweight design and excellent feature extraction capabilities, allows for efficient training and deployment on resource-constrained devices.
Apple vs Banana classification model with MobileNet architec
11039595
The "Object Detection with YOLOv8" project focuses on utilizing the YOLOv8 architecture for accurate and efficient object detection tasks. YOLOv8 (You Only Look Once version 8) is a state-of-the-art deep learning model known for its real-time object detection capabilities. This project aims to leverage YOLOv8 to detect and localize objects within images or video frames with high accuracy. The YOLOv8 algorithm uses a single neural network to process the entire image or frame, predicting bounding boxes and class probabilities for multiple objects simultaneously. This allows for efficient and real-time object detection, making it suitable for various applications, including video surveillance, autonomous vehicles, and image analysis.
Object Detection with YOLOv8
11039584
The "Time Series Forecasting" project focuses on analyzing and predicting future values in time-dependent data. Time series forecasting plays a vital role in various domains, allowing us to anticipate trends, patterns, and behavior exhibited by data over time. This project aims to develop robust and accurate models for forecasting future values based on historical data. By leveraging advanced time series analysis techniques and machine learning algorithms, we can extract valuable insights and make informed predictions in a wide range of applications. The repository contains datasets for training and evaluating the forecasting models. Additionally, it provides Jupyter notebooks that offer step-by-step implementations of different time series forecasting techniques. These notebooks include explanations, code snippets, and visualizations to help users understand and apply the methodologies effectively. The project also includes a directory
Time Series Forecasting

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Kokemus

Data Scientist

Programmers Force
tammik. 2023 - Voimassa
Experienced data scientist specializing in computer vision and natural language processing (NLP) at Programmer Force. Expertise in developing innovative solutions for image recognition, object detection, text analysis, and sentiment analysis. Skilled in implementing deep learning algorithms and utilizing popular frameworks like TensorFlow and PyTorch. Strong track record of delivering high-quality results in complex projects. Available for freelance opportunities.

Machine Learning Engineer

Air Labs Kicks, UET Lahore.
maalisk. 2022 - lokak. 2022 (7 kuukautta, 1 )
As a machine learning engineer at Air Labs Kicks, UET Lahore, I've gained valuable hands-on experience for 6 months. I've worked on cutting-edge projects, honing my skills in developing machine learning models, data preprocessing, and model evaluation. I've collaborated with a talented team, utilizing my expertise to deliver innovative solutions.

Koulutus

BSCS

Virtual University of Pakistan , Pakistan 2017 - 2021
(4 vuotta)

Pätevyydet

Machine Learning

Corvit Systems Lahore
2021
I have done my Machine Learning course from Corvit systems Lahore with A+ Grade.

Artificial Intelligence Course

Kicks, UET lahore
2022
I have done my Artificial Intelligence course from kicks, UET, Lahore with A+ Grade.

Ota yhteyttä käyttäjään Muhammad Rizwan A. työhösi liittyen

Kirjaudu sisään keskustellaksesi yksityiskohdista chatissa.

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