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$20 USD / hour
Flag of INDIA
kangra, india
$20 USD / hour
It's currently 8:18 AM here
Joined January 18, 2020
0 Recommendations

Rahul B.

@RahulBadhan

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4.4 (3 reviews)
2.6
2.6
$20 USD / hour
Flag of INDIA
kangra, india
$20 USD / hour
100%
Jobs Completed
95%
On Budget
25%
On Time
N/A
Repeat Hire Rate

Ml with python

Machine learning (ML) is a type of artificial intelligence which focuses on the development of computer programs which could change when exposed to a new data. It uses computer models and information obtained from past and previous data to aid classification, prediction and detection processes. This paper was designed to perform a review on some of the widely used classification algorithms and their application in breast cancer diagnosis. Feature dimensions can be reduced using the appropriate feature selection or feature extraction method. There are several methods used to reduce the dimensions of features in a dataset. Feature selection techniques involve selecting a subset of features from the original set of features . Feature extraction on the other hand aims at generating new features by merging the original features. This means that, they transform the features to artificial set and still retaining the information of the original dataset. Large number of features can affect the performance of a machine learning model. This work used four different methods to solve the high dimensionality problem. I made a classification model which predict that a patient has cancer or not. With the help of given data.
Freelancer Python Developers India

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Portfolio Items

Dataset provided includes labelled Cytological Images. GAN to be used along with augmentation
Data pre-processing should include both with and without Image augmentation.
Implement the following architectures for the given dataset in Google Colab.
1. AlexNet
2. VGG16
3. VGG19
4. GoogleNet
Comparative analysis of the used architectures with following plots in a document:
1. training accuracy vs epoch
2. validation accuracy vs epoch
3. test accuracy (scratch vs fine-tune)/epoch
4. confusion matrix without normalization
5. confusion matrix with normalization
6. accuracy score
7. Cross-Entropy loss vs epoch, train accuracy vs epoch, test accuracy vs epoch
8. model accuracy(accuracy versus epoch)
9. false positive
10. false negative
A simple web application (graphical user interface) for validation with options to choose the preferred architecture and display the results of prediction & classification type also implemented visualizations for respective architecture
A well-documented rep
cancer detection
Dataset provided includes labelled Cytological Images. GAN to be used along with augmentation
Data pre-processing should include both with and without Image augmentation.
Implement the following architectures for the given dataset in Google Colab.
1. AlexNet
2. VGG16
3. VGG19
4. GoogleNet
Comparative analysis of the used architectures with following plots in a document:
1. training accuracy vs epoch
2. validation accuracy vs epoch
3. test accuracy (scratch vs fine-tune)/epoch
4. confusion matrix without normalization
5. confusion matrix with normalization
6. accuracy score
7. Cross-Entropy loss vs epoch, train accuracy vs epoch, test accuracy vs epoch
8. model accuracy(accuracy versus epoch)
9. false positive
10. false negative
A simple web application (graphical user interface) for validation with options to choose the preferred architecture and display the results of prediction & classification type also implemented visualizations for respective architecture
A well-documented rep
cancer detection
Dataset provided includes labelled Cytological Images. GAN to be used along with augmentation
Data pre-processing should include both with and without Image augmentation.
Implement the following architectures for the given dataset in Google Colab.
1. AlexNet
2. VGG16
3. VGG19
4. GoogleNet
Comparative analysis of the used architectures with following plots in a document:
1. training accuracy vs epoch
2. validation accuracy vs epoch
3. test accuracy (scratch vs fine-tune)/epoch
4. confusion matrix without normalization
5. confusion matrix with normalization
6. accuracy score
7. Cross-Entropy loss vs epoch, train accuracy vs epoch, test accuracy vs epoch
8. model accuracy(accuracy versus epoch)
9. false positive
10. false negative
A simple web application (graphical user interface) for validation with options to choose the preferred architecture and display the results of prediction & classification type also implemented visualizations for respective architecture
A well-documented rep
cancer detection
Dataset provided includes labelled Cytological Images. GAN to be used along with augmentation
Data pre-processing should include both with and without Image augmentation.
Implement the following architectures for the given dataset in Google Colab.
1. AlexNet
2. VGG16
3. VGG19
4. GoogleNet
Comparative analysis of the used architectures with following plots in a document:
1. training accuracy vs epoch
2. validation accuracy vs epoch
3. test accuracy (scratch vs fine-tune)/epoch
4. confusion matrix without normalization
5. confusion matrix with normalization
6. accuracy score
7. Cross-Entropy loss vs epoch, train accuracy vs epoch, test accuracy vs epoch
8. model accuracy(accuracy versus epoch)
9. false positive
10. false negative
A simple web application (graphical user interface) for validation with options to choose the preferred architecture and display the results of prediction & classification type also implemented visualizations for respective architecture
A well-documented rep
cancer detection

Reviews

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Showing 1 - 3 out of 3 reviews
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5.0
$10.00 USD
Very professional and intelligent freelancer. He is open to new ideas and is very patient with discussing technical details. It was great working with him. Highly Recommended. Ready to work in the future always.
Python Software Architecture Machine Learning (ML)
I
Flag of Indra K. @indramary
3 months ago
4.2
$150.00 USD
he is good at work and he is good to communicate with customers be and very good at machine learning
Python Machine Learning (ML) Image Processing Video Processing Deep Learning
G
Flag of Osamah A. @GENERALOSAMA22
5 months ago
4.8
₹3,000.00 INR
Super Fast and knowledgeable person. Rahul is one of the best coders i have worked with. Will always recommend him. Deep Learning = RAHUL :)
Python Image Processing Tensorflow Keras Deep Learning
User Avatar
Flag of Rohit K. @RohitKasture2012
1 year ago

Education

Msc it

Lovely Professional University, India 2018 - 2021
(3 years)

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Python 3 Machine Learning (ML) 2 Deep Learning 2 Django Natural Language

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