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kasur, pakistan
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ML Soft Tech

@mza5ab8dc9be9a8c

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4,8 (95 отзывов(-а))
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Флаг PAKISTAN
kasur, pakistan
$50 USD / час
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Data Science | Machine Learning | Content Writing

Thanks for being here on our profile. We believe that our success is in customer satisfaction! We are now on FREELANCER.com to show our abilities and to help people in Machine Learning and Deep Learning and content writing! We have been working on the following projects: Machine Learning: ✅ Object Detection, Recognition, and Tracking ✅ Classification ✅ Time Series forecasting ✅ Data warehousing ✅ Data Mining ✅ Regression ✅ Clustering ✅ Dimensionality Reduction ✅ Recommender Systems ✅ Trading bot ✅ Data Scrapping ✅ Other ... Content Writing: ✅ We help you to write documents regarding machine learning and deep learning. We will deliver 100% required & quality work within a given time period!
Freelancer Python Developers Pakistan

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Элементы портфолио

I deal with all kinds of deep learning projects using Python Language. I'm the only freelancer who passed all 3 exams and got a medal. Looking forward!
Deep Learning Projects
I deal with all kinds of deep learning projects using Python Language. I'm the only freelancer who passed all 3 exams and got a medal. Looking forward!
Deep Learning Projects
I deal with all kinds of deep learning projects using Python Language. I'm the only freelancer who passed all 3 exams and got a medal. Looking forward!
Deep Learning Projects
I deal with all kinds of machine learning projects using Python Language. I'm the only freelancer who passed all 3 exams and got a medal. Looking forward!
Machine learning Projects
I deal with all kinds of machine learning projects using Python Language. I'm the only freelancer who passed all 3 exams and got a medal. Looking forward!
Machine learning Projects
I deal with all kinds of machine learning projects using Python Language. I'm the only freelancer who passed all 3 exams and got a medal. Looking forward!
Machine learning Projects
The objective of this project is to understand and take hands-on experience of implementation of machine learning models for customer segmentation over cloud and local machine. The dataset which will be use is related to customers and their purchase details. Multiple machine learning models will be built to perform predictive evaluation on the customer and their purchase related dataset. The dataset will be collected from UCI repository and all the required extraction, transformation and manipulation will be done through the machine learning models. Multiple models can be created in the field of customer analytics. In this project our main focus will be on customer segmentation using machine learning over cloud and local machine. Initially dataset that we took from UCI repository was untagged and that was tagged using clustering. After tagging, machine learning algorithm were applied in this study for customer segmentation.
Online Retail Data for Customer Segmentation
The objective of this project is to understand and take hands-on experience of implementation of machine learning models for customer segmentation over cloud and local machine. The dataset which will be use is related to customers and their purchase details. Multiple machine learning models will be built to perform predictive evaluation on the customer and their purchase related dataset. The dataset will be collected from UCI repository and all the required extraction, transformation and manipulation will be done through the machine learning models. Multiple models can be created in the field of customer analytics. In this project our main focus will be on customer segmentation using machine learning over cloud and local machine. Initially dataset that we took from UCI repository was untagged and that was tagged using clustering. After tagging, machine learning algorithm were applied in this study for customer segmentation.
Online Retail Data for Customer Segmentation
The objective of this project is to understand and take hands-on experience of implementation of machine learning models for customer segmentation over cloud and local machine. The dataset which will be use is related to customers and their purchase details. Multiple machine learning models will be built to perform predictive evaluation on the customer and their purchase related dataset. The dataset will be collected from UCI repository and all the required extraction, transformation and manipulation will be done through the machine learning models. Multiple models can be created in the field of customer analytics. In this project our main focus will be on customer segmentation using machine learning over cloud and local machine. Initially dataset that we took from UCI repository was untagged and that was tagged using clustering. After tagging, machine learning algorithm were applied in this study for customer segmentation.
Online Retail Data for Customer Segmentation
The objective of this project is to understand and take hands-on experience of implementation of machine learning models for customer segmentation over cloud and local machine. The dataset which will be use is related to customers and their purchase details. Multiple machine learning models will be built to perform predictive evaluation on the customer and their purchase related dataset. The dataset will be collected from UCI repository and all the required extraction, transformation and manipulation will be done through the machine learning models. Multiple models can be created in the field of customer analytics. In this project our main focus will be on customer segmentation using machine learning over cloud and local machine. Initially dataset that we took from UCI repository was untagged and that was tagged using clustering. After tagging, machine learning algorithm were applied in this study for customer segmentation.
Online Retail Data for Customer Segmentation
The objective of this project is to understand and take hands-on experience of implementation of machine learning models for customer segmentation over cloud and local machine. The dataset which will be use is related to customers and their purchase details. Multiple machine learning models will be built to perform predictive evaluation on the customer and their purchase related dataset. The dataset will be collected from UCI repository and all the required extraction, transformation and manipulation will be done through the machine learning models. Multiple models can be created in the field of customer analytics. In this project our main focus will be on customer segmentation using machine learning over cloud and local machine. Initially dataset that we took from UCI repository was untagged and that was tagged using clustering. After tagging, machine learning algorithm were applied in this study for customer segmentation.
Online Retail Data for Customer Segmentation
The objective of this project is to understand and take hands-on experience of implementation of machine learning models for customer segmentation over cloud and local machine. The dataset which will be use is related to customers and their purchase details. Multiple machine learning models will be built to perform predictive evaluation on the customer and their purchase related dataset. The dataset will be collected from UCI repository and all the required extraction, transformation and manipulation will be done through the machine learning models. Multiple models can be created in the field of customer analytics. In this project our main focus will be on customer segmentation using machine learning over cloud and local machine. Initially dataset that we took from UCI repository was untagged and that was tagged using clustering. After tagging, machine learning algorithm were applied in this study for customer segmentation.
Online Retail Data for Customer Segmentation
The objective of this project is to understand and take hands-on experience of implementation of machine learning models for customer segmentation over cloud and local machine. The dataset which will be use is related to customers and their purchase details. Multiple machine learning models will be built to perform predictive evaluation on the customer and their purchase related dataset. The dataset will be collected from UCI repository and all the required extraction, transformation and manipulation will be done through the machine learning models. Multiple models can be created in the field of customer analytics. In this project our main focus will be on customer segmentation using machine learning over cloud and local machine. Initially dataset that we took from UCI repository was untagged and that was tagged using clustering. After tagging, machine learning algorithm were applied in this study for customer segmentation.
Online Retail Data for Customer Segmentation
The objective of this project is to understand and take hands-on experience of implementation of machine learning models for customer segmentation over cloud and local machine. The dataset which will be use is related to customers and their purchase details. Multiple machine learning models will be built to perform predictive evaluation on the customer and their purchase related dataset. The dataset will be collected from UCI repository and all the required extraction, transformation and manipulation will be done through the machine learning models. Multiple models can be created in the field of customer analytics. In this project our main focus will be on customer segmentation using machine learning over cloud and local machine. Initially dataset that we took from UCI repository was untagged and that was tagged using clustering. After tagging, machine learning algorithm were applied in this study for customer segmentation.
Online Retail Data for Customer Segmentation
The objective of this project is to understand and take hands-on experience of implementation of machine learning models for customer segmentation over cloud and local machine. The dataset which will be use is related to customers and their purchase details. Multiple machine learning models will be built to perform predictive evaluation on the customer and their purchase related dataset. The dataset will be collected from UCI repository and all the required extraction, transformation and manipulation will be done through the machine learning models. Multiple models can be created in the field of customer analytics. In this project our main focus will be on customer segmentation using machine learning over cloud and local machine. Initially dataset that we took from UCI repository was untagged and that was tagged using clustering. After tagging, machine learning algorithm were applied in this study for customer segmentation.
Online Retail Data for Customer Segmentation
A Robust Deep Networks Based Multi-Camera Multi-Object Tracking System Simultaneous tracking of multiple objects is a state-of-art problem in the field of computer vision. Given n camera streams, determine who/what is where at all times. For tracking multiple objects, lots of problems can arise like occlusion of objects, overlapping of objects, and object identification with multiple cameras. Some recent work has proposed solutions to minimize these problems. The main goal of this research was to devise a multi-camera object tracking system for overcoming the challenges of occlusions, illumination, and overlapping by implementing deep neural networks. I participated in AI City Challenge 2020 and got the fourth rank in the competition with an IDF1 score of 0.4623.
Multi-Camera Multi-Object Tracking
A Robust Deep Networks Based Multi-Camera Multi-Object Tracking System Simultaneous tracking of multiple objects is a state-of-art problem in the field of computer vision. Given n camera streams, determine who/what is where at all times. For tracking multiple objects, lots of problems can arise like occlusion of objects, overlapping of objects, and object identification with multiple cameras. Some recent work has proposed solutions to minimize these problems. The main goal of this research was to devise a multi-camera object tracking system for overcoming the challenges of occlusions, illumination, and overlapping by implementing deep neural networks. I participated in AI City Challenge 2020 and got the fourth rank in the competition with an IDF1 score of 0.4623.
Multi-Camera Multi-Object Tracking
A Robust Deep Networks Based Multi-Camera Multi-Object Tracking System Simultaneous tracking of multiple objects is a state-of-art problem in the field of computer vision. Given n camera streams, determine who/what is where at all times. For tracking multiple objects, lots of problems can arise like occlusion of objects, overlapping of objects, and object identification with multiple cameras. Some recent work has proposed solutions to minimize these problems. The main goal of this research was to devise a multi-camera object tracking system for overcoming the challenges of occlusions, illumination, and overlapping by implementing deep neural networks. I participated in AI City Challenge 2020 and got the fourth rank in the competition with an IDF1 score of 0.4623.
Multi-Camera Multi-Object Tracking
A Robust Deep Networks Based Multi-Camera Multi-Object Tracking System Simultaneous tracking of multiple objects is a state-of-art problem in the field of computer vision. Given n camera streams, determine who/what is where at all times. For tracking multiple objects, lots of problems can arise like occlusion of objects, overlapping of objects, and object identification with multiple cameras. Some recent work has proposed solutions to minimize these problems. The main goal of this research was to devise a multi-camera object tracking system for overcoming the challenges of occlusions, illumination, and overlapping by implementing deep neural networks. I participated in AI City Challenge 2020 and got the fourth rank in the competition with an IDF1 score of 0.4623.
Multi-Camera Multi-Object Tracking
A Robust Deep Networks Based Multi-Camera Multi-Object Tracking System Simultaneous tracking of multiple objects is a state-of-art problem in the field of computer vision. Given n camera streams, determine who/what is where at all times. For tracking multiple objects, lots of problems can arise like occlusion of objects, overlapping of objects, and object identification with multiple cameras. Some recent work has proposed solutions to minimize these problems. The main goal of this research was to devise a multi-camera object tracking system for overcoming the challenges of occlusions, illumination, and overlapping by implementing deep neural networks. I participated in AI City Challenge 2020 and got the fourth rank in the competition with an IDF1 score of 0.4623.
Multi-Camera Multi-Object Tracking
A Robust Deep Networks Based Multi-Camera Multi-Object Tracking System Simultaneous tracking of multiple objects is a state-of-art problem in the field of computer vision. Given n camera streams, determine who/what is where at all times. For tracking multiple objects, lots of problems can arise like occlusion of objects, overlapping of objects, and object identification with multiple cameras. Some recent work has proposed solutions to minimize these problems. The main goal of this research was to devise a multi-camera object tracking system for overcoming the challenges of occlusions, illumination, and overlapping by implementing deep neural networks. I participated in AI City Challenge 2020 and got the fourth rank in the competition with an IDF1 score of 0.4623.
Multi-Camera Multi-Object Tracking
A Robust Deep Networks Based Multi-Camera Multi-Object Tracking System Simultaneous tracking of multiple objects is a state-of-art problem in the field of computer vision. Given n camera streams, determine who/what is where at all times. For tracking multiple objects, lots of problems can arise like occlusion of objects, overlapping of objects, and object identification with multiple cameras. Some recent work has proposed solutions to minimize these problems. The main goal of this research was to devise a multi-camera object tracking system for overcoming the challenges of occlusions, illumination, and overlapping by implementing deep neural networks. I participated in AI City Challenge 2020 and got the fourth rank in the competition with an IDF1 score of 0.4623.
Multi-Camera Multi-Object Tracking
A Robust Deep Networks Based Multi-Camera Multi-Object Tracking System Simultaneous tracking of multiple objects is a state-of-art problem in the field of computer vision. Given n camera streams, determine who/what is where at all times. For tracking multiple objects, lots of problems can arise like occlusion of objects, overlapping of objects, and object identification with multiple cameras. Some recent work has proposed solutions to minimize these problems. The main goal of this research was to devise a multi-camera object tracking system for overcoming the challenges of occlusions, illumination, and overlapping by implementing deep neural networks. I participated in AI City Challenge 2020 and got the fourth rank in the competition with an IDF1 score of 0.4623.
Multi-Camera Multi-Object Tracking
A Robust Deep Networks Based Multi-Camera Multi-Object Tracking System Simultaneous tracking of multiple objects is a state-of-art problem in the field of computer vision. Given n camera streams, determine who/what is where at all times. For tracking multiple objects, lots of problems can arise like occlusion of objects, overlapping of objects, and object identification with multiple cameras. Some recent work has proposed solutions to minimize these problems. The main goal of this research was to devise a multi-camera object tracking system for overcoming the challenges of occlusions, illumination, and overlapping by implementing deep neural networks. I participated in AI City Challenge 2020 and got the fourth rank in the competition with an IDF1 score of 0.4623.
Multi-Camera Multi-Object Tracking
A Robust Deep Networks Based Multi-Camera Multi-Object Tracking System Simultaneous tracking of multiple objects is a state-of-art problem in the field of computer vision. Given n camera streams, determine who/what is where at all times. For tracking multiple objects, lots of problems can arise like occlusion of objects, overlapping of objects, and object identification with multiple cameras. Some recent work has proposed solutions to minimize these problems. The main goal of this research was to devise a multi-camera object tracking system for overcoming the challenges of occlusions, illumination, and overlapping by implementing deep neural networks. I participated in AI City Challenge 2020 and got the fourth rank in the competition with an IDF1 score of 0.4623.
Multi-Camera Multi-Object Tracking
A Robust Deep Networks Based Multi-Camera Multi-Object Tracking System Simultaneous tracking of multiple objects is a state-of-art problem in the field of computer vision. Given n camera streams, determine who/what is where at all times. For tracking multiple objects, lots of problems can arise like occlusion of objects, overlapping of objects, and object identification with multiple cameras. Some recent work has proposed solutions to minimize these problems. The main goal of this research was to devise a multi-camera object tracking system for overcoming the challenges of occlusions, illumination, and overlapping by implementing deep neural networks. I participated in AI City Challenge 2020 and got the fourth rank in the competition with an IDF1 score of 0.4623.
Multi-Camera Multi-Object Tracking
In this project, we used Yahoo Finance APIs and collected data on different stocks. We calculated the different features and predicted the futures prices of stocks as well as with BUY and SELL signals. We have done a lot of things in this project.
Time Series Forecasting
In this project, we used Yahoo Finance APIs and collected data on different stocks. We calculated the different features and predicted the futures prices of stocks as well as with BUY and SELL signals. We have done a lot of things in this project.
Time Series Forecasting
In this project, we used Yahoo Finance APIs and collected data on different stocks. We calculated the different features and predicted the futures prices of stocks as well as with BUY and SELL signals. We have done a lot of things in this project.
Time Series Forecasting
In this project, we used Yahoo Finance APIs and collected data on different stocks. We calculated the different features and predicted the futures prices of stocks as well as with BUY and SELL signals. We have done a lot of things in this project.
Time Series Forecasting
In this project, we used Yahoo Finance APIs and collected data on different stocks. We calculated the different features and predicted the futures prices of stocks as well as with BUY and SELL signals. We have done a lot of things in this project.
Time Series Forecasting
In this project, we used Yahoo Finance APIs and collected data on different stocks. We calculated the different features and predicted the futures prices of stocks as well as with BUY and SELL signals. We have done a lot of things in this project.
Time Series Forecasting
In this project, we used Yahoo Finance APIs and collected data on different stocks. We calculated the different features and predicted the futures prices of stocks as well as with BUY and SELL signals. We have done a lot of things in this project.
Time Series Forecasting
In this project, we used Yahoo Finance APIs and collected data on different stocks. We calculated the different features and predicted the futures prices of stocks as well as with BUY and SELL signals. We have done a lot of things in this project.
Time Series Forecasting
In this project, we used Yahoo Finance APIs and collected data on different stocks. We calculated the different features and predicted the futures prices of stocks as well as with BUY and SELL signals. We have done a lot of things in this project.
Time Series Forecasting
In this project, we applied the NLP-based techniques and did the classification using the given dataset of the client. 
We applied an embedding layer for the text data and used a bi-directional LSTM model for the classification.
Text Classification

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$240,00 USD
The work was perfect and excellent .highly recommend
Python Algorithm Machine Learning (ML) Data Mining
+ еще 1
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Флаг Fawaz M. @kontm2588
4 месяца назад
5,0
$200,00 USD
Amazing working with ML Soft Tech , extremely detailed deliverables and super easy to contact i highly recommend again
Python Algorithm Machine Learning (ML) Data Mining
+ еще 1
K
Флаг Fawaz M. @kontm2588
4 месяца назад
5,0
£500,00 GBP
They are not experts and I did not get what I thought was correct work. I would not recommend for cryptocurrency project
Python Algorithm Machine Learning (ML) Data Mining Cryptocurrency
L
Флаг Lawrence G. @lawrenceGibbons1
10 месяцев назад
5,0
$40,00 USD
Yes. The hired freelancer met my expectations for this project.
Python Matlab and Mathematica Software Architecture Research Writing
Аватарка пользователя
Флаг Mona Ibrahim Ibrahim A. @monaabou
11 месяцев назад
5,0
₹14 250,00 INR
Great work! Submitted on time with great quality!
Python Software Architecture Statistics Machine Learning (ML) Statistical Analysis
M
Флаг Mitashi Haren M. @mitashi2506
11 месяцев назад

Опыт работы

Data Analyst

I&K Data Visualizer
мар. 2016 - Настоящее время
I'm working on large amounts of data: facts, figures, and number crunching. I see through the data and analyze it to find conclusions. I present data findings and translate the data into an understandable document. I look at the numbers, trends, and data and come to new conclusions based on the findings.

Machine Learning Engineer

New World
мар. 2015 - Настоящее время
Study and transform data science prototypes Design machine learning systems Research and implement appropriate ML algorithms and tools Develop machine learning applications according to requirements Select appropriate datasets and data representation methods Run machine learning tests and experiments Perform statistical analysis and fine-tuning using test results Train and retrain systems when necessary Extend existing ML libraries and frameworks Keep abreast of developments in the field

Web Developer

Jinnah Software House
февр. 2013 - мар. 2015 (2 года, 1 месяц)
I worked as a web developer(.NET).

Образование

PhD (Computer Vision and NLP)

University of California, Los Angeles, United States 2018 - 2022
(4 года)

MSCS (MPhil Computer Science)

COMSATS Institute of Information Technology, Pakistan 2015 - 2017
(2 года)

MCS (Master in Computer Science)

Superior College, Pakistan 2013 - 2015
(2 года)

Квалификация

Mathematics for Machine Learning: Multivariate Calculus (2020)

Coursera
2020
We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster.

Neural Networks and Deep Learning

Udemy
2018
I completed the "Neural Networks and Deep Learning" course with the state of art neural networks. I started with the introduction of neural network architectures and then Shallow Neural Network and Deep Neural Networks

Mathematics for Machine Learning: Multivariate Calculus

Coursera
2020
We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster.

Публикации

A Robust Deep Networks based Multi-Object Multi-Camera Tracking System for City Scale Traffic

CVPR2020
A Robust Deep Networks Based Multi-Camera Multi-Object Tracking System Simultaneous tracking of multiple objects is a state-of-art problem in the field of computer vision. The main goal of this research was to devise a multi-camera object tracking system for overcoming the challenges of occlusions, illumination, and overlapping by implementing deep neural networks. I participated in AI City Challenge 2020 and got the fourth rank in the competition with an IDF1 score of 0.4623.

Identification of Sentence Boundary from Ambiguous Text

CCL & NLP-NABD 2018
We have observed and tested previous work of many researchers who applied different techniques and algorithms (Feed-forward, maximum entropy, convolutional neural networks, and rule-based classification) for this task and got different accuracy percentage on various corpora. In this paper, we disambiguate the sentence boundary by applying the maximum entropy method and stop words removal. By implementation of these methods, we got 99% results on a brown corpus which contained on 52000 sentences.

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Связано с Facebook

Сертификаты

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Топ-навыки

Python
94
Machine Learning (ML) 81 Data Mining 53 Algorithm 47 Data Scraping 41

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