Λογότυπο FreelancerΠως ΛειτουργείΑναζήτησε Εργασίες
Είσοδος Εγγραφή
Ανάρτηση Εργασίας
Εικόνα εξωφύλλου προφίλ
Τώρα ακολουθείς τον/την
Σφάλμα στην ακολουθία χρήστη.
Αυτός ο χρήστης δεν επιτρέπει σε χρήστες να τον ακολουθούν.
Έχεις ήδη ακολουθήσει αυτό τον χρήστη.
Το πλάνο συνδρομή σου επιτρέπει μόνο 0 ακόλουθους. Κάνε αναβάθμιση εδώ.
Δεν ακολουθείς τον/την
Σφάλμα κατάργησης ακόλουθου χρήστη.
Έχεις προτείνει επιτυχώς τον/την
Σφάλμα πρότασης χρήστη.
Κάτι πήγε στραβά. Παρακαλούμε ανανέωσε τη σελίδα και δοκίμασε ξανά.
Επιτυχώς επαληθευμένο Email.
Avatar Χρήστη
$50 USD / ώρα
Σημαία της UZBEKISTAN
tashkent, uzbekistan
$50 USD / ώρα
Τώρα είναι 10:56 μ.μ. εδώ
Έγινε μέλος Νοεμβρίου 27, 2021
1 Πρόταση

Samidullo A.

@Samidullo

 5,0 (11 αξιολογήσεις)
4,0
4,0
69%
69%
$50 USD / ώρα
Σημαία της UZBEKISTAN
tashkent, uzbekistan
$50 USD / ώρα
100%
Ολοκληρωμένες Δουλειές
89%
Εντός Προϋπολογισμού
93%
Εντός Χρόνου
5%
Βαθμός Επανάληψης Πρόσληψης

Data scientist/ Data analytic / SQL / Tabeau

Welcome to my Freelancer Profile! I am a Machine learning Engineer whose focus is in Multi / Logistic/Linear Regression. I enjoy developing new and novel models as well as creating infrastructure for automation and ease-of-use. Data of all types interest me. I have experience developing Machine learning applications for a variety of hardware architectures, including mobile devices.. Working with me you will get best practice in source code management, version control, and security access to your code base. My technology stack is listed in long-form below. Core: - Linux/Ubuntu - Python 3 Data Manipulation: - Numpy - Pandas - OpenCV - PIL/Pillow Machine Learning - Scikit-learn -SciPy Deep Learning: - Tensorflow - Keras - PyTorch Data mining : -Weka Data Visualization: -Tableau - Matplotlib - Seaborn Thanks for your consideration!
Freelancer Python Developers Uzbekistan
Προβολή όλων Python Developers σε Uzbekistan

Επικοινώνησε με τον/την Samidullo A. για τη δουλειά σου

Συνδέσου για να συζητήσεις τις λεπτομέρειες μέσω τις συνομιλίας.

Πορτφόλιο

9832985
A5/1 is the strong version of the encryption algorithm used by about 130 million GSM customers in Europe to protect the over-the-air privacy of their cellular voice and data communication. The best published attacks against it require between 240 and 245 steps. This level of security makes it vulnerable to hardware-based attacks by large organizations, but not to software-based attacks on multiple targets by hackers. In this paper we describe new attacks on A5/1, which are based on subtle flaws in the tap structure of the registers, their noninvertible clocking mechanism, and their frequent resets. After a 248 parallelizable data preparation stage (which has to be carried out only once), the actual attacks can be carried out in real time on a single PC.
A5/1 Cryptography algorithm
A5/1 is the strong version of the encryption algorithm used by about 130 million GSM customers in Europe to protect the over-the-air privacy of their cellular voice and data communication. The best published attacks against it require between 240 and 245 steps. This level of security makes it vulnerable to hardware-based attacks by large organizations, but not to software-based attacks on multiple targets by hackers. In this paper we describe new attacks on A5/1, which are based on subtle flaws in the tap structure of the registers, their noninvertible clocking mechanism, and their frequent resets. After a 248 parallelizable data preparation stage (which has to be carried out only once), the actual attacks can be carried out in real time on a single PC.
A5/1 Cryptography algorithm
9832980
Data encryption standard (DES) has been found vulnerable against very powerful attacks and therefore, the popularity of DES has been found slightly on the decline. DES is a block cipher and encrypts data in blocks of size of 64 bits each, which means 64 bits of plain text goes as the input to DES, which produces 64 bits of ciphertext. The same algorithm and key are used for encryption and decryption, with minor differences. The key length is 56 bits. The basic idea is shown in the figure.
Data encryption standard (DES)
Data encryption standard (DES) has been found vulnerable against very powerful attacks and therefore, the popularity of DES has been found slightly on the decline. DES is a block cipher and encrypts data in blocks of size of 64 bits each, which means 64 bits of plain text goes as the input to DES, which produces 64 bits of ciphertext. The same algorithm and key are used for encryption and decryption, with minor differences. The key length is 56 bits. The basic idea is shown in the figure.
Data encryption standard (DES)
9831453
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018. Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives. Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem. An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018. Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives. Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem. An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018. Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives. Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem. An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018. Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives. Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem. An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
9717574
Task is to write a program in any programming language supported on our Linux CSE machines that will decrypt as much of the message as possible using the fact that Alice and Bob reused their one-time pad for all of the six messages that Eve stored. This will simply involve reading in the file appropriately (i.e., using two hexadecimal values for each encrypted character) and then applying some techniques to decrypt the ciphertexts. You may assume that you have six ciphertexts, each with 60 characters expressed using their hexadecimal ASCII values (i.e., 2 hexadecimal values or 8 bits). The one-time pad used to create each ciphertext is exactly the length of the plaintext message (in bytes, that is, 120 bytes). Also, the original plaintexts contain only upper- and lowercase alphabetic characters with spaces (i.e., no special characters or punctuation).
Decryption One-Time-Pad encrypted code without Passwork
Task is to write a program in any programming language supported on our Linux CSE machines that will decrypt as much of the message as possible using the fact that Alice and Bob reused their one-time pad for all of the six messages that Eve stored. This will simply involve reading in the file appropriately (i.e., using two hexadecimal values for each encrypted character) and then applying some techniques to decrypt the ciphertexts. You may assume that you have six ciphertexts, each with 60 characters expressed using their hexadecimal ASCII values (i.e., 2 hexadecimal values or 8 bits). The one-time pad used to create each ciphertext is exactly the length of the plaintext message (in bytes, that is, 120 bytes). Also, the original plaintexts contain only upper- and lowercase alphabetic characters with spaces (i.e., no special characters or punctuation).
Decryption One-Time-Pad encrypted code without Passwork
Task is to write a program in any programming language supported on our Linux CSE machines that will decrypt as much of the message as possible using the fact that Alice and Bob reused their one-time pad for all of the six messages that Eve stored. This will simply involve reading in the file appropriately (i.e., using two hexadecimal values for each encrypted character) and then applying some techniques to decrypt the ciphertexts. You may assume that you have six ciphertexts, each with 60 characters expressed using their hexadecimal ASCII values (i.e., 2 hexadecimal values or 8 bits). The one-time pad used to create each ciphertext is exactly the length of the plaintext message (in bytes, that is, 120 bytes). Also, the original plaintexts contain only upper- and lowercase alphabetic characters with spaces (i.e., no special characters or punctuation).
Decryption One-Time-Pad encrypted code without Passwork
9682149
Working with ML models : J48 Tree, Naive Bayes Simple, RBF Network, IB1
Using Weka knowledge flow
9682111
K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry.
Unsupervised Kmeans algorithm used to clusted Iris data
K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry.
Unsupervised Kmeans algorithm used to clusted Iris data
K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry.
Unsupervised Kmeans algorithm used to clusted Iris data
9832985
A5/1 is the strong version of the encryption algorithm used by about 130 million GSM customers in Europe to protect the over-the-air privacy of their cellular voice and data communication. The best published attacks against it require between 240 and 245 steps. This level of security makes it vulnerable to hardware-based attacks by large organizations, but not to software-based attacks on multiple targets by hackers. In this paper we describe new attacks on A5/1, which are based on subtle flaws in the tap structure of the registers, their noninvertible clocking mechanism, and their frequent resets. After a 248 parallelizable data preparation stage (which has to be carried out only once), the actual attacks can be carried out in real time on a single PC.
A5/1 Cryptography algorithm
A5/1 is the strong version of the encryption algorithm used by about 130 million GSM customers in Europe to protect the over-the-air privacy of their cellular voice and data communication. The best published attacks against it require between 240 and 245 steps. This level of security makes it vulnerable to hardware-based attacks by large organizations, but not to software-based attacks on multiple targets by hackers. In this paper we describe new attacks on A5/1, which are based on subtle flaws in the tap structure of the registers, their noninvertible clocking mechanism, and their frequent resets. After a 248 parallelizable data preparation stage (which has to be carried out only once), the actual attacks can be carried out in real time on a single PC.
A5/1 Cryptography algorithm
9832980
Data encryption standard (DES) has been found vulnerable against very powerful attacks and therefore, the popularity of DES has been found slightly on the decline. DES is a block cipher and encrypts data in blocks of size of 64 bits each, which means 64 bits of plain text goes as the input to DES, which produces 64 bits of ciphertext. The same algorithm and key are used for encryption and decryption, with minor differences. The key length is 56 bits. The basic idea is shown in the figure.
Data encryption standard (DES)
Data encryption standard (DES) has been found vulnerable against very powerful attacks and therefore, the popularity of DES has been found slightly on the decline. DES is a block cipher and encrypts data in blocks of size of 64 bits each, which means 64 bits of plain text goes as the input to DES, which produces 64 bits of ciphertext. The same algorithm and key are used for encryption and decryption, with minor differences. The key length is 56 bits. The basic idea is shown in the figure.
Data encryption standard (DES)
9831453
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018. Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives. Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem. An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018. Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives. Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem. An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018. Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives. Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem. An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018. Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives. Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem. An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
9717574
Task is to write a program in any programming language supported on our Linux CSE machines that will decrypt as much of the message as possible using the fact that Alice and Bob reused their one-time pad for all of the six messages that Eve stored. This will simply involve reading in the file appropriately (i.e., using two hexadecimal values for each encrypted character) and then applying some techniques to decrypt the ciphertexts. You may assume that you have six ciphertexts, each with 60 characters expressed using their hexadecimal ASCII values (i.e., 2 hexadecimal values or 8 bits). The one-time pad used to create each ciphertext is exactly the length of the plaintext message (in bytes, that is, 120 bytes). Also, the original plaintexts contain only upper- and lowercase alphabetic characters with spaces (i.e., no special characters or punctuation).
Decryption One-Time-Pad encrypted code without Passwork
Task is to write a program in any programming language supported on our Linux CSE machines that will decrypt as much of the message as possible using the fact that Alice and Bob reused their one-time pad for all of the six messages that Eve stored. This will simply involve reading in the file appropriately (i.e., using two hexadecimal values for each encrypted character) and then applying some techniques to decrypt the ciphertexts. You may assume that you have six ciphertexts, each with 60 characters expressed using their hexadecimal ASCII values (i.e., 2 hexadecimal values or 8 bits). The one-time pad used to create each ciphertext is exactly the length of the plaintext message (in bytes, that is, 120 bytes). Also, the original plaintexts contain only upper- and lowercase alphabetic characters with spaces (i.e., no special characters or punctuation).
Decryption One-Time-Pad encrypted code without Passwork
Task is to write a program in any programming language supported on our Linux CSE machines that will decrypt as much of the message as possible using the fact that Alice and Bob reused their one-time pad for all of the six messages that Eve stored. This will simply involve reading in the file appropriately (i.e., using two hexadecimal values for each encrypted character) and then applying some techniques to decrypt the ciphertexts. You may assume that you have six ciphertexts, each with 60 characters expressed using their hexadecimal ASCII values (i.e., 2 hexadecimal values or 8 bits). The one-time pad used to create each ciphertext is exactly the length of the plaintext message (in bytes, that is, 120 bytes). Also, the original plaintexts contain only upper- and lowercase alphabetic characters with spaces (i.e., no special characters or punctuation).
Decryption One-Time-Pad encrypted code without Passwork
9682149
Working with ML models : J48 Tree, Naive Bayes Simple, RBF Network, IB1
Using Weka knowledge flow
9682111
K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry.
Unsupervised Kmeans algorithm used to clusted Iris data
K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry.
Unsupervised Kmeans algorithm used to clusted Iris data
K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry.
Unsupervised Kmeans algorithm used to clusted Iris data

Αξιολογήσεις

Οι αλλαγές αποθηκεύτηκαν
Προβολή 1 - 5 από 11 αξιολογήσεις
 Φιλτράρισμα αξιολογήσεων ανά:
5,0
£80,00 GBP
Data Analytics -- 4
Very impressed with the quality of work delivered! would highly recommend this freelancer for any data analytics project.
Data Processing
Excel
Data Mining
Data Analytics
Data Cleansing
A
Σημαία της Jack A.
@Azeez10
•
2 χρόνια πριν
5,0
₹2.000,00 INR
Jupyter Notebook Python expert
Good job done on the project
Python
Software Architecture
Statistics
Machine Learning (ML)
H
Σημαία της Harsh K.
@harshal37
•
2 χρόνια πριν
5,0
₹1.500,00 INR
Project for Samidullo A.
It is very easy to work with you. You are very polite and professional.
Matlab and Mathematica
Mathematics
Linear Programming
MATLAB
+1 περισσότερα
V
Σημαία της Simon A.
@VIPCorp
•
2 χρόνια πριν
5,0
$75,00 USD
Project for Samidullo A.
Sami deliver work on time, doing work professionally, happy to work with this freelancer!!
Django
Data Visualization
Data Architecture
Computer Vision
+1 περισσότερα
Avatar Χρήστη
Σημαία της Waqas A.
@vicky66
•
2 χρόνια πριν
5,0
₹23.150,00 INR
Python Expert -- 3
Good Work. Thanks! Have a nice day.
Python
Software Architecture
V
Σημαία της Simon A.
@VIPCorp
•
2 χρόνια πριν

Προσόντα

AI engineering

IBM
2021
About this Course This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

Επικοινώνησε με τον/την Samidullo A. για τη δουλειά σου

Συνδέσου για να συζητήσεις τις λεπτομέρειες μέσω τις συνομιλίας.

Επαληθεύσεις

Προτιμώμενος Freelancer
Η Ταυτότητα Επαληθεύτηκε
Επαληθευμένη Πληρωμή
Το Τηλέφωνο Επαληθεύτηκε
Το Email Επαληθεύτηκε
Το Facebook Συνδέθηκε

Πιστοποιήσεις

preferredfreelancer-1.png Preferred Freelancer Program SLA 1 92%

Κορυφαίες Ικανότητες

Python 6
Data Mining 5
Machine Learning (ML) 4
Data Science 4
Algorithm 3

Αναζήτησε Παρόμοιους Freelancers

Python Developers in Uzbekistan
Python Developers
Data Mining Experts
Machine Learning Experts

Αναζήτησε Παρόμοιες Προβολές

Python
Data Mining
Machine Learning (ML)
Data Science
Προηγούμενος Χρήστης Νέος Χρήστης
Η πρόσκληση έχει σταλεί επιτυχώς!
Ευχαριστούμε! Σου έχουμε στείλει ένα email με ένα σύνδεσμο για να διεκδικήσεις τη δωρεάν πίστωση σου.
Κάτι πήγε στραβά κατά την προσπάθεια αποστολής του email σου. Παρακαλούμε δοκίμασε ξανά.
Λογότυπο Freelancer Greece / ελληνικά Βοήθεια & Υποστήριξη FreelancerΚατηγορίεςΕργασίεςΔιαγωνισμοί FreelancersEnterpriseΣυνδρομήΠρόγραμμα Προτιμώμενου FreelancerΔιαχείριση ΕργασιώνΤοπικές Δουλειές Photo AnywhereΈκθεσηAPI για DevelopersΛάβε ΕπαλήθευσηDesktop Εφαρμογή Σχετικά Σχετικά με εμάςΠως ΛειτουργείΑσφάλειαΕπενδυτήςΧάρτης ΣελίδαςΙστορίεςΝέαΌμαδαΒραβείαΑνακοινώσεις ΤύπουΚαριέρες Όροι Πολιτική Προστασίας Προσωπικών ΔεδομένωνΌροι και ΠροϋποθέσειςΠολιτική Πνευματικών Δικαιωμάτων Κώδικας ΕπικοινωνίαςΧρεώσεις και ΠρομήθειεςΣυνεργάτεςEscrow.comLoadshiftWarrior Forum Εφαρμογές λογότυπο Apple App StoreΛογότυπο Google Play
Η αντιγραφή στο πληκτρολόγιο απέτυχε, παρακαλούμε δοκίμασε ξανά αφού τροποποιήσεις τις άδειες σου.
Αντιγράφηκε στο πληκτρολόγιο.
Εγγεγραμμένοι Χρήστες Συνολικές Αναρτημένες Δουλειές
Freelancer ® is a registered Trademark of Freelancer Technology Pty Limited (ACN 142 189 759)
Copyright © 2024 Freelancer Technology Pty Limited (ACN 142 189 759)
Φόρτωση προεπισκόπησης
Δόθηκε πρόσβαση για Geolocation.
Η σύνδεση σου έχει λήξει και τώρα έχεις αποσυνδεθεί. Παρακαλούμε συνδέσου ξανά.