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Käyttäjän avatar
$30 USD / tunti
Maan BANGLADESH lippu
rajshahi, bangladesh
$30 USD / tunti
Aika on tällä hetkellä 9:45 ap. täällä
Liittynyt lokakuuta 15, 2017
1 Suositus

Shamim M.

@shamimjibon

 5,0 (5 arvostelua)
5,2
5,2
100%
100%
$30 USD / tunti
Maan BANGLADESH lippu
rajshahi, bangladesh
$30 USD / tunti
80 %
Suoritetut työt
100 %
Budjetin mukaisesti
100 %
Aikataulussa
33 %
Uudelleenpalkkausaste

Machine Learning Freelancer

Hello! I am Shamim. I am a Machine Learning freelancer. I have a workable knowledge in this field. I have a good grip on building Neural networks models, training the model, applying new models to get satisfactory results. Please feel free to reach me for any kind of ML/DL projects.
Freelancer Python Developers Bangladesh
Katso kaikki Python Developers paikassa Bangladesh

Ota yhteyttä käyttäjään Shamim M. työhösi liittyen

Kirjaudu sisään keskustellaksesi yksityiskohdista chatissa.

Portfolio

9052138
This portfolio is about the "Football Match Outcome Prediction". Here, I have used a football dataset named "LaLiga" and predicted different outcomes. For training the model, I have simply use the ANN model. The dataset has been provided in the "Dataset" folder. Initially, the dataset has 10 features/columns and 9662 rows. The name of the columns of the dataset are- • Season • Date • Home Team • Away Team • HTR - Half Time Result • FTR - Full-Time Result • HTHG - Half Time Home Goals • HTAG - Half Time Away Goals • FTHG - Full-Time Home Goals • FTAG - Full Time Away Goals
Football Match Outcomes Prediction
This portfolio is about the "Football Match Outcome Prediction". Here, I have used a football dataset named "LaLiga" and predicted different outcomes. For training the model, I have simply use the ANN model. The dataset has been provided in the "Dataset" folder. Initially, the dataset has 10 features/columns and 9662 rows. The name of the columns of the dataset are- • Season • Date • Home Team • Away Team • HTR - Half Time Result • FTR - Full-Time Result • HTHG - Half Time Home Goals • HTAG - Half Time Away Goals • FTHG - Full-Time Home Goals • FTAG - Full Time Away Goals
Football Match Outcomes Prediction
This portfolio is about the "Football Match Outcome Prediction". Here, I have used a football dataset named "LaLiga" and predicted different outcomes. For training the model, I have simply use the ANN model. The dataset has been provided in the "Dataset" folder. Initially, the dataset has 10 features/columns and 9662 rows. The name of the columns of the dataset are- • Season • Date • Home Team • Away Team • HTR - Half Time Result • FTR - Full-Time Result • HTHG - Half Time Home Goals • HTAG - Half Time Away Goals • FTHG - Full-Time Home Goals • FTAG - Full Time Away Goals
Football Match Outcomes Prediction
This portfolio is about the "Football Match Outcome Prediction". Here, I have used a football dataset named "LaLiga" and predicted different outcomes. For training the model, I have simply use the ANN model. The dataset has been provided in the "Dataset" folder. Initially, the dataset has 10 features/columns and 9662 rows. The name of the columns of the dataset are- • Season • Date • Home Team • Away Team • HTR - Half Time Result • FTR - Full-Time Result • HTHG - Half Time Home Goals • HTAG - Half Time Away Goals • FTHG - Full-Time Home Goals • FTAG - Full Time Away Goals
Football Match Outcomes Prediction
8934717
I have done this project to classify various symptoms of a diabetic patient in their eyes. Those symptoms are needed to be extracted from fundus image. I have used various image processing techniques to extract features from images and applied transfer learning for classification. Also, I have applied a CNN model to analyze the performance of the model. Nonetheless, it has achieved an accuracy of 98.74% on the training set.
Diabetic Retinopathy Claasification
I have done this project to classify various symptoms of a diabetic patient in their eyes. Those symptoms are needed to be extracted from fundus image. I have used various image processing techniques to extract features from images and applied transfer learning for classification. Also, I have applied a CNN model to analyze the performance of the model. Nonetheless, it has achieved an accuracy of 98.74% on the training set.
Diabetic Retinopathy Claasification
8936146
The MNIST dataset is an acronym that stands for the Modified National Institute of Standards and Technology dataset. It is a dataset of 60,000 small square 28×28 pixel grayscale images of handwritten single digits between 0 and 9. The task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. It is a widely used and deeply understood dataset and, for the most part, is “solved.” Top-performing models are deep learning convolutional neural networks that achieve a classification accuracy of above 99%, with an error rate between 0.4 %and 0.2% on the hold-out test dataset.
MNIST DIGIT Recognition
The MNIST dataset is an acronym that stands for the Modified National Institute of Standards and Technology dataset. It is a dataset of 60,000 small square 28×28 pixel grayscale images of handwritten single digits between 0 and 9. The task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. It is a widely used and deeply understood dataset and, for the most part, is “solved.” Top-performing models are deep learning convolutional neural networks that achieve a classification accuracy of above 99%, with an error rate between 0.4 %and 0.2% on the hold-out test dataset.
MNIST DIGIT Recognition
8934701
I have done Bangla sign language classification using Machine learning classifiers and also integrated various pre-trained Deep CNN models to extract features from it. I have also used handcrafted features like HOG, SIFT, and LBP for feature extraction. This model gave me an accuracy of 100%.
Sign Language Digits Classification
9052138
This portfolio is about the "Football Match Outcome Prediction". Here, I have used a football dataset named "LaLiga" and predicted different outcomes. For training the model, I have simply use the ANN model. The dataset has been provided in the "Dataset" folder. Initially, the dataset has 10 features/columns and 9662 rows. The name of the columns of the dataset are- • Season • Date • Home Team • Away Team • HTR - Half Time Result • FTR - Full-Time Result • HTHG - Half Time Home Goals • HTAG - Half Time Away Goals • FTHG - Full-Time Home Goals • FTAG - Full Time Away Goals
Football Match Outcomes Prediction
This portfolio is about the "Football Match Outcome Prediction". Here, I have used a football dataset named "LaLiga" and predicted different outcomes. For training the model, I have simply use the ANN model. The dataset has been provided in the "Dataset" folder. Initially, the dataset has 10 features/columns and 9662 rows. The name of the columns of the dataset are- • Season • Date • Home Team • Away Team • HTR - Half Time Result • FTR - Full-Time Result • HTHG - Half Time Home Goals • HTAG - Half Time Away Goals • FTHG - Full-Time Home Goals • FTAG - Full Time Away Goals
Football Match Outcomes Prediction
This portfolio is about the "Football Match Outcome Prediction". Here, I have used a football dataset named "LaLiga" and predicted different outcomes. For training the model, I have simply use the ANN model. The dataset has been provided in the "Dataset" folder. Initially, the dataset has 10 features/columns and 9662 rows. The name of the columns of the dataset are- • Season • Date • Home Team • Away Team • HTR - Half Time Result • FTR - Full-Time Result • HTHG - Half Time Home Goals • HTAG - Half Time Away Goals • FTHG - Full-Time Home Goals • FTAG - Full Time Away Goals
Football Match Outcomes Prediction
This portfolio is about the "Football Match Outcome Prediction". Here, I have used a football dataset named "LaLiga" and predicted different outcomes. For training the model, I have simply use the ANN model. The dataset has been provided in the "Dataset" folder. Initially, the dataset has 10 features/columns and 9662 rows. The name of the columns of the dataset are- • Season • Date • Home Team • Away Team • HTR - Half Time Result • FTR - Full-Time Result • HTHG - Half Time Home Goals • HTAG - Half Time Away Goals • FTHG - Full-Time Home Goals • FTAG - Full Time Away Goals
Football Match Outcomes Prediction
8934717
I have done this project to classify various symptoms of a diabetic patient in their eyes. Those symptoms are needed to be extracted from fundus image. I have used various image processing techniques to extract features from images and applied transfer learning for classification. Also, I have applied a CNN model to analyze the performance of the model. Nonetheless, it has achieved an accuracy of 98.74% on the training set.
Diabetic Retinopathy Claasification
I have done this project to classify various symptoms of a diabetic patient in their eyes. Those symptoms are needed to be extracted from fundus image. I have used various image processing techniques to extract features from images and applied transfer learning for classification. Also, I have applied a CNN model to analyze the performance of the model. Nonetheless, it has achieved an accuracy of 98.74% on the training set.
Diabetic Retinopathy Claasification
8936146
The MNIST dataset is an acronym that stands for the Modified National Institute of Standards and Technology dataset. It is a dataset of 60,000 small square 28×28 pixel grayscale images of handwritten single digits between 0 and 9. The task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. It is a widely used and deeply understood dataset and, for the most part, is “solved.” Top-performing models are deep learning convolutional neural networks that achieve a classification accuracy of above 99%, with an error rate between 0.4 %and 0.2% on the hold-out test dataset.
MNIST DIGIT Recognition
The MNIST dataset is an acronym that stands for the Modified National Institute of Standards and Technology dataset. It is a dataset of 60,000 small square 28×28 pixel grayscale images of handwritten single digits between 0 and 9. The task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. It is a widely used and deeply understood dataset and, for the most part, is “solved.” Top-performing models are deep learning convolutional neural networks that achieve a classification accuracy of above 99%, with an error rate between 0.4 %and 0.2% on the hold-out test dataset.
MNIST DIGIT Recognition
8934701
I have done Bangla sign language classification using Machine learning classifiers and also integrated various pre-trained Deep CNN models to extract features from it. I have also used handcrafted features like HOG, SIFT, and LBP for feature extraction. This model gave me an accuracy of 100%.
Sign Language Digits Classification

Arvostelut

Muutokset tallennettu
Näytetään 1 - 5 / 5 arvostelua
Suodata arvosteluja:
5,0
$1 470,00 USD
Stock price prediction modeling using Prophet
I have known Shamim and worked with him for several years. We have done many projects together and I look forward to doing more projects with him in the future. He's persistent and resilient. He's also smart and does not give up easily. Great experience overall.
Python
Arduino
Image Processing
Computer Vision
+1 enemmän
S
Maan lippu Fadi F.
@sfphoenixai
•
10 kuukautta sitten
5,0
$2 085,00 USD
Binary classification for breast cancer medical images
Another great experience with Shamim. Will work with him again, and I highly recommend him.
Python
Arduino
Image Processing
Computer Vision
+1 enemmän
S
Maan lippu Fadi F.
@sfphoenixai
•
vuosi sitten
5,0
$360,00 USD
Train Deep Learning model on language translation
Shamim is a great person to work with. He is smart, resilient and very good at following instructions and figuring out how to solve the problem at hand. Already hiring Shamim for a new project after this one.
Python
Machine Learning (ML)
Deep Learning
S
Maan lippu Fadi F.
@sfphoenixai
•
2 vuotta sitten
4,0
$149,00 USD
Medical Imaging Project - Need Deep Learning Engineer
Shamim is a great person to work with. He is resilient and does not give up easily on tough tasks!
Python
Image Processing
Computer Vision
Deep Learning
S
Maan lippu Fadi F.
@sfphoenixai
•
3 vuotta sitten
5,0
$100,00 USD
Annotate dental radiographs by tooth, tissue and background
Shamim is a skilled data scientist who never seems to give up! He is very resourceful and a highly ethical person. i will work with him again for sure.
Arduino
Artificial Intelligence
Image Processing
Computer Vision
+1 enemmän
S
Maan lippu Fadi F.
@sfphoenixai
•
3 vuotta sitten

Kokemus

Software Engineer (NLP)

Zibew
maalisk. 2022 - Voimassa
I am working at Zibew as a Software Engineer (NLP). My key responsibility is to implement different projects by using AI and NLP.

Artificial Intelligence Engineer

Opus Technology Limited
heinäk. 2021 - helmik. 2022 (7 kuukautta, 1 )
I have currently joined this company. It is a great experience for me to join them and work in AI fields.

Intern

Northern Electricity Supply Company Ltd.
helmik. 2021 - huhtik. 2021 (2 kuukautta, 3 päivää)
It was an internship program for my undergraduate course.

Koulutus

BSc (Engg.) in Electrical & Electronic Engineering, (EEE).

Hajee Mohammad Danesh University of Science and Technology, Bangladesh 2016 - 2020
(4 vuotta)

Pätevyydet

Introduction to Computer Vision with Watson and OpenCV

Coursera
2020
Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries such as self-driving cars, robotics, augmented reality, face detection in law enforcement agencies. In this beginner-friendly course you will understand about computer vision, and will learn about its various applications across many industries.

Convolutional Neural Networks in Tensorflow

Coursera
2020
It was a paid course from Coursera. I have learned various methods of CNN using TensorFlow.

Machine Learning

Coursera
2020
It was a paid course from Coursera. The course instructor was Professor Andrew Ng. I loved this course. It has enriched my basic of machine learning.

Julkaisut

Comp. of Hand. and DNN Feature Extraction for Recognizing Bangla Sign Digits Exp. ML Classifiers

IEEE
Sign Language is the main communication medium for people who have a difficulty with speaking or hearing. To communicate with other deaf or healthy people, they use sign language. Deaf and mute people of Bangladesh and India use Bangla sign language to fulfill their communication needs. But, the learning process of sign language is time consuming and arduous.

Rice Disease Identification and Classification by integrating SVM with DCNN

IEEE
Over the world, rice is one of the most extensively consumed staple foods. But due to various diseases of rice, farmers can't yield rice according to their expectations. Therefore, the quality, quantity, and production of rice are being interrupted. It is difficult for the farmers to diagnose diseases, because of the lack of knowledge and unavailability of professional specialists. It also requires much time to manually identifying and classifying rice diseases.

Performance Analysis of Machine Learning Techniques for Microscopic Bacteria Image Classification

IEEE Explore
Microscopic bacterial image classification is of great significance in medical science in order to diagnosis numerous fatal diseases caused by bacteria. But, professional individuals are required for the manual identification process of bacteria which is time-consuming and laborious.

Ota yhteyttä käyttäjään Shamim M. työhösi liittyen

Kirjaudu sisään keskustellaksesi yksityiskohdista chatissa.

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Parhaat taidot

Python 4
Deep Learning 4
Image Processing 3
Computer Vision 3
Arduino 2

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