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$35 USD / hour
Flag of INDIA
saharanpur, india
$35 USD / hour
It's currently 12:43 PM here
Joined November 4, 2012
3 Recommendations

Mohd T.

@tausy

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0.0 (106 reviews)
6.4
6.4
$35 USD / hour
Flag of INDIA
saharanpur, india
$35 USD / hour
96%
Jobs Completed
82%
On Budget
97%
On Time
16%
Repeat Hire Rate

ML, AI, Data Science, Python, Hadoop, Databases

- Data Scientist with over 7 years of industry experience, knowledge and understanding of Machine Learning, Data Analysis, Big Data/Hadoop, ETL, and Databases. - I hold a Master's degree in Data Science from Trinity College Dublin and a Bachelor's degree in Computer Science. - Currently, working as a data scientist with one of the world's largest banking and financial firm. - Solid understanding and expertise in analyzing and maintaining large datasets. - Honed my skills in Data Ingestion, Data Analysis, Data Migration, Data Consolidation, Data Processing, Data Visualization, and Data Mining. - In my 7 years of career, I worked primarily on Predictive Modeling, Machine Learning, and Hadoop to deliver cutting-edge predictive models in Healthcare, Aviation, and Financial sectors. - Extensive experience in building machine learning applications using Python and its ML stack libraries including NumPy, Pandas, Scikit-Learn, Matplotlib, etc. - Development and implementation experience of building data analytics pipelines and ML systems using PySpark on big data. - Worked extensively on Big Data and Hadoop stack tools including but not limited to Sqoop, Flume, Oozie, Hive, Impala, HDFS, and Map Reduce. - Worked on numerous projects of SQL, PL/SQL, ETL, Informatica, SSIS, and Informatica DIH for years. - Proficient in Java, and Python programming languages. Also, work on R statistical language. - Current areas of interest include Data Science, Data Analytics, Machine Learning, Predictive Modeling, Knowledge Discovery from Databases(KDD), Data Mining, Web Mining, and Information Retrieval.
Freelancer Python Developers India

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Portfolio Items

The goal of this project is to create a system that can detect whether or not someone is wearing a mask. CCTV cameras are used to record images or real-time video footage. Facial features are retrieved from the images or video footage and utilized to identify the mask on the face. We are attempting to detect the face mask using the capabilities of convolutional neural networks in this application. We're also trying to count the number of people who wear a proper face-covering against those who don't.
Mask Detection/Real-time Human Counting with Deep Learning
The goal of this project is to create a system that can detect whether or not someone is wearing a mask. CCTV cameras are used to record images or real-time video footage. Facial features are retrieved from the images or video footage and utilized to identify the mask on the face. We are attempting to detect the face mask using the capabilities of convolutional neural networks in this application. We're also trying to count the number of people who wear a proper face-covering against those who don't.
Mask Detection/Real-time Human Counting with Deep Learning
The goal of this project is to create a system that can detect whether or not someone is wearing a mask. CCTV cameras are used to record images or real-time video footage. Facial features are retrieved from the images or video footage and utilized to identify the mask on the face. We are attempting to detect the face mask using the capabilities of convolutional neural networks in this application. We're also trying to count the number of people who wear a proper face-covering against those who don't.
Mask Detection/Real-time Human Counting with Deep Learning
The information age has led to information overload. A simple Google search for - apple renders almost 55,50,00,000 results ranging from all walks of life. The problem is compounded by the fact that even if a relevant page is encountered the amount of textual data present in a single page can be quite overwhelming. Further increasing the complexity, if the time is limited, reading the entire document becomes very difficult. Even skimming is difficult as there can be a problem of loss of context. And many times we find ourselves in need of only the gist or summary of articles that we read. 
This system does exactly this, whatever you are reading online, if it is more than you can handle, simply paste the URL/text into the application, and you will get only a few sentences as the summary of the entire article. A simple and easy-to-use application making your reading experiences on the web a more productive one.
SumGen - A Webpage Summary Generator
The information age has led to information overload. A simple Google search for - apple renders almost 55,50,00,000 results ranging from all walks of life. The problem is compounded by the fact that even if a relevant page is encountered the amount of textual data present in a single page can be quite overwhelming. Further increasing the complexity, if the time is limited, reading the entire document becomes very difficult. Even skimming is difficult as there can be a problem of loss of context. And many times we find ourselves in need of only the gist or summary of articles that we read. 
This system does exactly this, whatever you are reading online, if it is more than you can handle, simply paste the URL/text into the application, and you will get only a few sentences as the summary of the entire article. A simple and easy-to-use application making your reading experiences on the web a more productive one.
SumGen - A Webpage Summary Generator
The information age has led to information overload. A simple Google search for - apple renders almost 55,50,00,000 results ranging from all walks of life. The problem is compounded by the fact that even if a relevant page is encountered the amount of textual data present in a single page can be quite overwhelming. Further increasing the complexity, if the time is limited, reading the entire document becomes very difficult. Even skimming is difficult as there can be a problem of loss of context. And many times we find ourselves in need of only the gist or summary of articles that we read. 
This system does exactly this, whatever you are reading online, if it is more than you can handle, simply paste the URL/text into the application, and you will get only a few sentences as the summary of the entire article. A simple and easy-to-use application making your reading experiences on the web a more productive one.
SumGen - A Webpage Summary Generator
The information age has led to information overload. A simple Google search for - apple renders almost 55,50,00,000 results ranging from all walks of life. The problem is compounded by the fact that even if a relevant page is encountered the amount of textual data present in a single page can be quite overwhelming. Further increasing the complexity, if the time is limited, reading the entire document becomes very difficult. Even skimming is difficult as there can be a problem of loss of context. And many times we find ourselves in need of only the gist or summary of articles that we read. 
This system does exactly this, whatever you are reading online, if it is more than you can handle, simply paste the URL/text into the application, and you will get only a few sentences as the summary of the entire article. A simple and easy-to-use application making your reading experiences on the web a more productive one.
SumGen - A Webpage Summary Generator
The information age has led to information overload. A simple Google search for - apple renders almost 55,50,00,000 results ranging from all walks of life. The problem is compounded by the fact that even if a relevant page is encountered the amount of textual data present in a single page can be quite overwhelming. Further increasing the complexity, if the time is limited, reading the entire document becomes very difficult. Even skimming is difficult as there can be a problem of loss of context. And many times we find ourselves in need of only the gist or summary of articles that we read. 
This system does exactly this, whatever you are reading online, if it is more than you can handle, simply paste the URL/text into the application, and you will get only a few sentences as the summary of the entire article. A simple and easy-to-use application making your reading experiences on the web a more productive one.
SumGen - A Webpage Summary Generator
The information age has led to information overload. A simple Google search for - apple renders almost 55,50,00,000 results ranging from all walks of life. The problem is compounded by the fact that even if a relevant page is encountered the amount of textual data present in a single page can be quite overwhelming. Further increasing the complexity, if the time is limited, reading the entire document becomes very difficult. Even skimming is difficult as there can be a problem of loss of context. And many times we find ourselves in need of only the gist or summary of articles that we read. 
This system does exactly this, whatever you are reading online, if it is more than you can handle, simply paste the URL/text into the application, and you will get only a few sentences as the summary of the entire article. A simple and easy-to-use application making your reading experiences on the web a more productive one.
SumGen - A Webpage Summary Generator
The information age has led to information overload. A simple Google search for - apple renders almost 55,50,00,000 results ranging from all walks of life. The problem is compounded by the fact that even if a relevant page is encountered the amount of textual data present in a single page can be quite overwhelming. Further increasing the complexity, if the time is limited, reading the entire document becomes very difficult. Even skimming is difficult as there can be a problem of loss of context. And many times we find ourselves in need of only the gist or summary of articles that we read. 
This system does exactly this, whatever you are reading online, if it is more than you can handle, simply paste the URL/text into the application, and you will get only a few sentences as the summary of the entire article. A simple and easy-to-use application making your reading experiences on the web a more productive one.
SumGen - A Webpage Summary Generator
The information age has led to information overload. A simple Google search for - apple renders almost 55,50,00,000 results ranging from all walks of life. The problem is compounded by the fact that even if a relevant page is encountered the amount of textual data present in a single page can be quite overwhelming. Further increasing the complexity, if the time is limited, reading the entire document becomes very difficult. Even skimming is difficult as there can be a problem of loss of context. And many times we find ourselves in need of only the gist or summary of articles that we read. 
This system does exactly this, whatever you are reading online, if it is more than you can handle, simply paste the URL/text into the application, and you will get only a few sentences as the summary of the entire article. A simple and easy-to-use application making your reading experiences on the web a more productive one.
SumGen - A Webpage Summary Generator
The information age has led to information overload. A simple Google search for - apple renders almost 55,50,00,000 results ranging from all walks of life. The problem is compounded by the fact that even if a relevant page is encountered the amount of textual data present in a single page can be quite overwhelming. Further increasing the complexity, if the time is limited, reading the entire document becomes very difficult. Even skimming is difficult as there can be a problem of loss of context. And many times we find ourselves in need of only the gist or summary of articles that we read. 
This system does exactly this, whatever you are reading online, if it is more than you can handle, simply paste the URL/text into the application, and you will get only a few sentences as the summary of the entire article. A simple and easy-to-use application making your reading experiences on the web a more productive one.
SumGen - A Webpage Summary Generator
Created this utility to split the excel files with custom logic provided by the client. The utility is built using Java tech stack including UI and delivered as an executable jar file which could be invoked by double-clicking on it.
Custom Java Excel Splitter Utility
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning
Created and successfully delivered Loan Defaulter Prediction System which leverages the power of Machine learning models(supervised) and tries to predict whether a particular loan will default or not. As part of this project, I have handled the class im-balance problem, compared the performance of various machine learning supervised models including Logistic Regression, Support Vector Machines(SVM), Decision trees, and Random Forests. Also, utilized the 10-fold cross-validation strategy to avoid the problems of underfitting/overfitting of the model. Finally, hyperparameter tuning has been done to fine-tune the performance of the selected model.
Loan DefaulterPrediction System using Machine Learning

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$350.00 SGD
Hired him to work on one of my projects. He was able to deliver the project proposal, project poster, artefact and report ahead of time. Guided me all the way when setting up the environment and running the program. Friendly approach made it easier to deal with him.
Python Software Architecture Report Writing Machine Learning (ML) Statistical Analysis
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Flag of Albin V. @albinvarghese
2 days ago
5.0
$80.00 USD
Mohd T. He does an excellent job, he is responsible and absolutely an expert on the subject. 100% recommended
SQL
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Flag of F. O. @fortizclavijo
4 months ago
4.8
$475.00 USD
He clearly understands the tasks he has to perform and gives space for discussion, last but not least delivered on time.
Java Engineering MySQL
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Flag of Kristaq P. @kristians007
4 months ago
5.0
£750.00 GBP
Mohd delivered satisfactory work ahead of deadline and is easy to communicate and work with!!
Hadoop Scala Hive Spark Amazon S3
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Flag of Amal A. @LinaSal
5 months ago
5.0
₹15,000.00 INR
Really impressed and liked to work with Freelancer. Much appericiated his working stretegies, communication and deliverable of his work on time. Most Recommanded.
Python Matlab and Mathematica LaTeX Machine Learning (ML)
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Flag of Chintan S. @cvsoni22
5 months ago

Experience

Data Scientist

Citibank Europe
Dec 2019 - Present
Working as a data scientist in AI/ML team.

Hadoop/Machine Learning Developer

Opera Solutions
Sep 2017 - Present
Working on Hadoop Ecosystem in combination with python/machine learning to deliver predictive models.

Hadoop Developer

Tata Global Delivery Center SA, Montevideo, Uruguay, SA
Apr 2016 - Aug 2017 (1 year, 4 months)
Worked on Hadoop ecosystem to deliver cutting edge predictive models using Sqoop, Flume, Oozie, Hive, Map Reduce

Education

MSc Data Science

Trinity College, Dublin, Ireland 2018 - 2019
(1 year)

Bachelor Of Technology (Computer Engineering)

Jamia Millia Islamia, India 2009 - 2013
(4 years)

Qualifications

Certificate in Healthcare

Tata Business Domain Academy
2014

Oracle Database Certified SQL Expert

Oracle University
2015
SQL proficiency test certificate provided by Oracle

Oracle Database Certified PL/SQL Expert

Oracle University
2015
Pl/SQL proficiency test certificate provided by Oracle

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Top Skills

Python 72 Java 58 Big Data Sales 44 Hadoop 39 Machine Learning (ML) 15

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