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$15 USD / hour
Flag of INDIA
nadiad, india
$15 USD / hour
It's currently 11:37 AM here
Joined January 16, 2016
4 Recommendations

Bhargav D.

@bhargavdave

 4.9 (29 reviews)
4.0
4.0
92%
92%
$15 USD / hour
Flag of INDIA
nadiad, india
$15 USD / hour
84%
Jobs Completed
71%
On Budget
58%
On Time
5%
Repeat Hire Rate

Machine Learning and Deep Learning Expert

I just passed [login to view URL] with a Machine learning specialization from DAIICT, Gandhinagar. Currently, I am a member of the IRNLP lab at DAIICT and Junior Research Fellow (JRF) in a project MHRD sponsored project with a joint consortium of IIT Kharagpur, IIT Bombay, IIT Patna, DA-IICT and AU-KBC. I also published a paper, "Query specific graph-based query reformulation using UMLS for clinical information access" in the Journal of Biomedical Informatics. Recently I got 3rd rank in shared task on offensive language identification in Dravidian languages in DravidianLangTech@EACL2021 and 2nd rank in shared task on hope speech detection for equality, Diversity, and Inclusion. I am one of the organizers of the shared task on event detection from news in Indian languages (EDNIL) @ FIRE2020. Also I am organizer of Forum for Information Retrieval Evaluation (FIRE).
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Portfolio

10296949
Sentiment Analysis of Review of Review using Logistic Regression, Random Forest, SVM, XgBoost, LSTM etc
Sentiment Analysis of Review
Sentiment Analysis of Review of Review using Logistic Regression, Random Forest, SVM, XgBoost, LSTM etc
Sentiment Analysis of Review
Sentiment Analysis of Review of Review using Logistic Regression, Random Forest, SVM, XgBoost, LSTM etc
Sentiment Analysis of Review
10296943
Customer Churn Prediction using Machine Learning and Deep Learning Algo
Customer Churn Prediction
Customer Churn Prediction using Machine Learning and Deep Learning Algo
Customer Churn Prediction
8942492
Below SVM Kernal implemented form scrach in this Project 1)Gaussian kernel 2)inear kernel 3)Polynomial Kernel 4)Sigmoid Kernel 5)Multiquadric Kernel 6)Cauchy kernel 7)TStudent Kernel
SVM Kernal Kernal From Scrach
8942477
For Fake User Detection happen in three part, 1) Extraction of profile features 2) Random Forest (RF), Naïve Bayes (NB) and Decision Tree (DT) used to determine the fake and genuine User. 3) Calculate and Compare of the accuracy rates across the results of all techniques.
Fake User Classfication
For Fake User Detection happen in three part, 1) Extraction of profile features 2) Random Forest (RF), Naïve Bayes (NB) and Decision Tree (DT) used to determine the fake and genuine User. 3) Calculate and Compare of the accuracy rates across the results of all techniques.
Fake User Classfication
6000392
Supplier static website
Website for Supplier Compney
Supplier static website
Website for Supplier Compney
Supplier static website
Website for Supplier Compney
10296949
Sentiment Analysis of Review of Review using Logistic Regression, Random Forest, SVM, XgBoost, LSTM etc
Sentiment Analysis of Review
Sentiment Analysis of Review of Review using Logistic Regression, Random Forest, SVM, XgBoost, LSTM etc
Sentiment Analysis of Review
Sentiment Analysis of Review of Review using Logistic Regression, Random Forest, SVM, XgBoost, LSTM etc
Sentiment Analysis of Review
10296943
Customer Churn Prediction using Machine Learning and Deep Learning Algo
Customer Churn Prediction
Customer Churn Prediction using Machine Learning and Deep Learning Algo
Customer Churn Prediction
8942492
Below SVM Kernal implemented form scrach in this Project 1)Gaussian kernel 2)inear kernel 3)Polynomial Kernel 4)Sigmoid Kernel 5)Multiquadric Kernel 6)Cauchy kernel 7)TStudent Kernel
SVM Kernal Kernal From Scrach
8942477
For Fake User Detection happen in three part, 1) Extraction of profile features 2) Random Forest (RF), Naïve Bayes (NB) and Decision Tree (DT) used to determine the fake and genuine User. 3) Calculate and Compare of the accuracy rates across the results of all techniques.
Fake User Classfication
For Fake User Detection happen in three part, 1) Extraction of profile features 2) Random Forest (RF), Naïve Bayes (NB) and Decision Tree (DT) used to determine the fake and genuine User. 3) Calculate and Compare of the accuracy rates across the results of all techniques.
Fake User Classfication
6000392
Supplier static website
Website for Supplier Compney
Supplier static website
Website for Supplier Compney
Supplier static website
Website for Supplier Compney

Reviews

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Showing 1 - 5 out of 29 reviews
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5.0
$70.00 USD
Implement text classifier - 2 classes
Great and fast job. Freelancer very responsive :) A++++++++
Python
Machine Learning (ML)
Artificial Intelligence
NLP
User Avatar
Flag of Krzysztof W.
@krzysztof36
•
6 months ago
1.0
₹3,000.00 INR
Humour Classification and Prediction Model
He was unable to complete the project due to some reasons but informed me of that just one day before the deadline
Python
Report Writing
Machine Learning (ML)
Deep Learning
NLP
User Avatar
Flag of Rishabh T.
@rishabht97
•
1 year ago
1.0
$10.00 USD
Project 34872702 has been deleted
***Freelancer.com Admin has removed this review due to inappropriate content - IONW-2611-KWWF***
T
Flag of Olea Jacquelyn P.
@tori100
•
2 years ago
5.0
$20.00 USD
K_Fold cross validation
He is good and too professional
Python
Machine Learning (ML)
Deep Learning
User Avatar
Flag of Neha G.
@gtmneha
•
2 years ago
5.0
₹14,000.00 INR
Web Scrapping and Sentiment analysis using NLP and LSTM method
Best support and timely work
Python
Web Scraping
Machine Learning (ML)
NLP
User Avatar
Flag of GSFGMICROLOG PVT LTD
@GSFG123
•
2 years ago

Experience

Junior Research Assistant

Dhirubhai Ambani Institute of Information and Communication Technology
Aug 2019 - Present
A platform for Cross-lingual and Multilingual Event Monitoring in Indian Languages. The project aims to build a cross-lingual, multi-lingual, multi-source and multi-domain platform for information extraction (IE) in Indian languages (ILs) to extract and classify events of interest from news media and to demonstrate the application in a few domains. This project is subpart of the project named “Imprint” which is funded by MHRD (Ministry of Human Resource and Development)

Web Devloper

A.live Mind
Jan 2017 - Jul 2018 (1 year, 6 months)
I am working in a.live mind as Web Developer from last six month.I work on many websites as front end and backend developer in the company.Mainly i use Html,CSS,Javascript,PHP,Jquery for web design. I use PHP codigneter framework in back end.I am very well in it now.

Education

M.tech in ICT with Machine Learning Specialization

Dhirubhai Ambani Institute of Information and Communication Technology, India 2018 - 2020
(2 years)

B.tech Information Tecnology

Dharmsinh Desai Institute of Technology, India 2013 - 2017
(4 years)

Publications

IRNLP_DAIICT@DravidianLangTech-EACL2021:Offensive Language identification in Dravidian Languages

2021
This paper presents the participation of the IRNLPDAIICT team from the Information Retrieval and Natural Language Processing lab at DA-IICT, India in DravidianLangTech-EACL2021 Offensive Language identification in Dravidian Languages. https://www.aclweb.org/anthology/2021.dravidianlangtech-1.37/

Overview of the FIRE 2020 EDNIL track: Event Detection from News in Indian Languages

2020
The goal of FIRE 2020 EDNIL track was to create a framework which could be used to detect events from news articles in English, Hindi, Bengali, Marathi and Tamil. The track consisted of two tasks:(i) Identifying a piece of text from news articles that contains an event (Event Identification).(ii) Creating an event frame from the news article (Event Frame Extraction).

IRNLP_DAIICT@LT-EDI-EACL2021: Hope Speech detection in Code Mixed text using TF-IDF & MuRIL

2021
This paper presents the participation of the IRNLP_DAIICT team from the Information Retrieval and Natural Language Processing lab at DA-IICT, India in LT-EDI@EACL2021 Hope Speech Detection task. https://www.aclweb.org/anthology/2021.ltedi-1.15/

FIRE 2020 EDNIL Track: Event Detection from News in Indian Languages

FIRE 2020: Forum for Information Retrieval Evaluation
The goal of the FIRE 2020 EDNIL track was to create a framework that could be used to detect events from news articles in English, Hindi, Bengali, Marathi, and Tamil. The track consisted of two tasks: (I) Identifying a piece of text from news articles that contain an event(Event Identification). (ii) Creating an event frame from the news article (Event Frame Extraction). The events that were identified in the Event Identification task were Man-made Disasters and Natural disasters.

Query specific graph-based query reformulation using UMLS for clinical information access

Journal of Biomedical Informatics
This paper explores the usage and impact of UMLS for entity-based query reformulation in biomedical document retrieval. A novel graph-based approach for query reformulation using UMLS is described herein which queries are expanded using biomedical entities. The proposed method considers UMLS entities from a query with their related entities identified by UMLS and constructs a query-specific graph of biomedical entities for term selection.

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Top Skills

Machine Learning (ML) 22
Deep Learning 4
NLP 3

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