Logo FreelancerComment cela fonctionneParcourir les emplois Se connecter S'inscrire Publier un projetProfile cover photo
Vous suivez désormais .
Erreur de suivi de l'utilisateur.
Cet utilisateur n'utilise pas les utilisateurs à le suivre.
Vous suivez déjà cet utilisateur.
Votre plan d'adhésion ne permet que 0 suivis. Améliorez ici.
L'utilisateur n'est désormais plus suivi.
Erreur lors de l'arrêt du suivi de l'utilisateur.
Vous avez désormais recommandé
Erreur lors de la recommendation de l'utilisateur.
Une erreur a eu lieu. Veuillez rafraîchir la page et réessayer.
E-mail désormais vérifié.
Avatar de l'utilisateur
$30 USD / heure
Drapeau de INDIA
mumbai, india
$30 USD / heure
Il est actuellement 5:24 AM ici
Membre depuis le août 13, 2017
5 Recommandations

Ujjwal K.

@ujjwal1996

5,0 (56 commentaires)
5,7
5,7
$30 USD / heure
Drapeau de INDIA
mumbai, india
$30 USD / heure
97 %
Travaux complétés
96 %
Suivant le budget
97 %
Dans les temps
26 %
Taux de réembauche

ML | Python | Data Science | Power BI | API

Thank you for visiting my profile. ★★★★ My service-providing skill sets include but are not limited to ★★★★ ✅ ENGINEERING AND TECHNOLOGY ✔️ Python, R, MATLAB, MS Excel ✔️ AI, ML, and Algorithms (Data Structures, Neural Networks, Data Science, NLP) ✔️ AI and ML Framework (TensorFlow, Keras, RNN, CNN, GAN, DNN, ...) ✔️ Probability and Statistics (Hypothesis testing, Forecasting, T-test, ANOVA, ...) ✔️ Data Analysis (Python Pandas, Jupyter Notebook, Anaconda) ✔️ Technical Writing ✔️ Web Scraping (BeautifulSoup, Selenium, requests, urllib, ...) ✔️ OpenCV (Object Segmentation, Object Detection, Image Processing, ...) ✔️ Optimizations (Genetic Algorithms, Swarm Optimization, Ant Colony Optimization, ...) ✔️ Mechanical and Electrical Engineering (Signal Processing, Control Systems, Robotics, CFD, Mechanics of Solids, ...) ------------------------------------------------------------------------------------------------------------------------------ ➤➤ I can spend full time on Freelancer and serve 24x7. ➤➤ Rest assured with 100% accuracy and efficiency on my part. ➤➤ I will provide you the project before the deadline and I am comfortable with negotiation in pricing. Let us build a great relationship for the successful implementation of your project. Best Regards
Freelancer Python Developers India

Contactez Ujjwal K. concernant votre emploi

Connectez-vous pour discuter des détails via la messagerie.

Éléments du portfolio

To forecast the travel times of the assigned route arc on every working
day for the whole current 2020 year (excluding January 1st, December
25th and every Sunday) based on the previous data.

Methodology:
The entire project is divided into 3 steps:
1. Pre-processing the data
2. Performing statistical analysis on the data
3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
To forecast the travel times of the assigned route arc on every working
day for the whole current 2020 year (excluding January 1st, December
25th and every Sunday) based on the previous data.

Methodology:
The entire project is divided into 3 steps:
1. Pre-processing the data
2. Performing statistical analysis on the data
3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
To forecast the travel times of the assigned route arc on every working
day for the whole current 2020 year (excluding January 1st, December
25th and every Sunday) based on the previous data.

Methodology:
The entire project is divided into 3 steps:
1. Pre-processing the data
2. Performing statistical analysis on the data
3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
To forecast the travel times of the assigned route arc on every working
day for the whole current 2020 year (excluding January 1st, December
25th and every Sunday) based on the previous data.

Methodology:
The entire project is divided into 3 steps:
1. Pre-processing the data
2. Performing statistical analysis on the data
3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
To forecast the travel times of the assigned route arc on every working
day for the whole current 2020 year (excluding January 1st, December
25th and every Sunday) based on the previous data.

Methodology:
The entire project is divided into 3 steps:
1. Pre-processing the data
2. Performing statistical analysis on the data
3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature
vector of size 5

GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the
objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature
vector of size 5

GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the
objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature
vector of size 5

GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the
objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature
vector of size 5

GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the
objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature
vector of size 5

GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the
objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature
vector of size 5

GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the
objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature
vector of size 5

GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the
objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature
vector of size 5

GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the
objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
The paper demonstrates one of the major application of CV which is Facial
expression classification through facial emotions using Convolution Neural Network
(CNN). 

We have also utilized Graphics Processing Unit (GPU) computation (available
by notebook in the Google colab) so that we can accelerate the training process. 

Now we have built this CNN model with the help of Python using a well known Tensorflow module
called Keras. 

Different types of expressions that can be classified by our model is based on
fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial
expression classification through facial emotions using Convolution Neural Network
(CNN). 

We have also utilized Graphics Processing Unit (GPU) computation (available
by notebook in the Google colab) so that we can accelerate the training process. 

Now we have built this CNN model with the help of Python using a well known Tensorflow module
called Keras. 

Different types of expressions that can be classified by our model is based on
fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial
expression classification through facial emotions using Convolution Neural Network
(CNN). 

We have also utilized Graphics Processing Unit (GPU) computation (available
by notebook in the Google colab) so that we can accelerate the training process. 

Now we have built this CNN model with the help of Python using a well known Tensorflow module
called Keras. 

Different types of expressions that can be classified by our model is based on
fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial
expression classification through facial emotions using Convolution Neural Network
(CNN). 

We have also utilized Graphics Processing Unit (GPU) computation (available
by notebook in the Google colab) so that we can accelerate the training process. 

Now we have built this CNN model with the help of Python using a well known Tensorflow module
called Keras. 

Different types of expressions that can be classified by our model is based on
fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial
expression classification through facial emotions using Convolution Neural Network
(CNN). 

We have also utilized Graphics Processing Unit (GPU) computation (available
by notebook in the Google colab) so that we can accelerate the training process. 

Now we have built this CNN model with the help of Python using a well known Tensorflow module
called Keras. 

Different types of expressions that can be classified by our model is based on
fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial
expression classification through facial emotions using Convolution Neural Network
(CNN). 

We have also utilized Graphics Processing Unit (GPU) computation (available
by notebook in the Google colab) so that we can accelerate the training process. 

Now we have built this CNN model with the help of Python using a well known Tensorflow module
called Keras. 

Different types of expressions that can be classified by our model is based on
fer2013 dataset.
Convolution Neural Network: Facial Expression classification
• To derive the stiffness matrix for a bar element.
• To illustrate how to solve a bar assemblage by the direct
stiffness method.
• To introduce guidelines for selecting displacement
functions.
• To describe the concept of transformation of vectors in
two different coordinate systems in the plane.
• To derive the stiffness matrix for a bar arbitrarily oriented
in the plane.
• To demonstrate how to compute stress for a bar in the
plane.
• To show how to solve a plane truss problem.
• To develop the transformation matrix in threedimensional space and show how to use it to derive the
stiffness matrix for a bar arbitrarily oriented in space.
• To demonstrate the solution of space trusses.
FEM Analysis using Python
• To derive the stiffness matrix for a bar element.
• To illustrate how to solve a bar assemblage by the direct
stiffness method.
• To introduce guidelines for selecting displacement
functions.
• To describe the concept of transformation of vectors in
two different coordinate systems in the plane.
• To derive the stiffness matrix for a bar arbitrarily oriented
in the plane.
• To demonstrate how to compute stress for a bar in the
plane.
• To show how to solve a plane truss problem.
• To develop the transformation matrix in threedimensional space and show how to use it to derive the
stiffness matrix for a bar arbitrarily oriented in space.
• To demonstrate the solution of space trusses.
FEM Analysis using Python
• To derive the stiffness matrix for a bar element.
• To illustrate how to solve a bar assemblage by the direct
stiffness method.
• To introduce guidelines for selecting displacement
functions.
• To describe the concept of transformation of vectors in
two different coordinate systems in the plane.
• To derive the stiffness matrix for a bar arbitrarily oriented
in the plane.
• To demonstrate how to compute stress for a bar in the
plane.
• To show how to solve a plane truss problem.
• To develop the transformation matrix in threedimensional space and show how to use it to derive the
stiffness matrix for a bar arbitrarily oriented in space.
• To demonstrate the solution of space trusses.
FEM Analysis using Python
• To derive the stiffness matrix for a bar element.
• To illustrate how to solve a bar assemblage by the direct
stiffness method.
• To introduce guidelines for selecting displacement
functions.
• To describe the concept of transformation of vectors in
two different coordinate systems in the plane.
• To derive the stiffness matrix for a bar arbitrarily oriented
in the plane.
• To demonstrate how to compute stress for a bar in the
plane.
• To show how to solve a plane truss problem.
• To develop the transformation matrix in threedimensional space and show how to use it to derive the
stiffness matrix for a bar arbitrarily oriented in space.
• To demonstrate the solution of space trusses.
FEM Analysis using Python
Use historical data to predict:

▪ future sales (Time series analysis)
▪ The probability of adding a new fund in the future
▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict:

▪ future sales (Time series analysis)
▪ The probability of adding a new fund in the future
▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict:

▪ future sales (Time series analysis)
▪ The probability of adding a new fund in the future
▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict:

▪ future sales (Time series analysis)
▪ The probability of adding a new fund in the future
▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict:

▪ future sales (Time series analysis)
▪ The probability of adding a new fund in the future
▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict:

▪ future sales (Time series analysis)
▪ The probability of adding a new fund in the future
▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict:

▪ future sales (Time series analysis)
▪ The probability of adding a new fund in the future
▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict:

▪ future sales (Time series analysis)
▪ The probability of adding a new fund in the future
▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Found the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data

Pattern finding was based upon RFM analysis which helped to create customer segments

Provided identified segments to the company so that they can devise a strategy to deal with such segments
RFM Analysis
Found the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data

Pattern finding was based upon RFM analysis which helped to create customer segments

Provided identified segments to the company so that they can devise a strategy to deal with such segments
RFM Analysis
Found the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data

Pattern finding was based upon RFM analysis which helped to create customer segments

Provided identified segments to the company so that they can devise a strategy to deal with such segments
RFM Analysis
Found the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data

Pattern finding was based upon RFM analysis which helped to create customer segments

Provided identified segments to the company so that they can devise a strategy to deal with such segments
RFM Analysis

Commentaires

Modifications enregistrées
Montre1 - 5 sur 50+ commentaires
Filtrer les commentaires par : 5,0
$100,00 USD
Very excellent work, definitely gonna hire him again.
Python Data Processing Natural Language
A
Drapeau de Awadh A. @awadk
il y a 16 jours
5,0
$140,00 USD
Professional and Helpful.
Excel Statistics R Programming Language Statistical Analysis
C
Drapeau de Micheal A. @coolguy157
il y a 3 mois
5,0
₹5 000,00 INR
He is professional in the work he take and he submitted the work before time
Python Data Analytics Data Analysis
A
Drapeau de Abhinandan J. @Abhinandan456
il y a 9 mois
5,0
£430,00 GBP
Professional and helpful. Could not ask for more!
JavaScript Finance Financial Analysis Corporate Income Tax
+1 de plus
N
Drapeau de Lais W. @Naithan23
il y a 10 mois
5,0
₹14 000,00 INR
On time completion. Expert
Python Machine Learning (ML) Mathematics Deep Learning
+1 de plus
D
Drapeau de Developer D. @developerdev18
il y a 1 an

Expérience

Research associate

National University of Singapore
juin 2019 - août 2019 (2 mois, 2 jours)
Quantified peristaltic motion of soft robot using object segmentation and multi task learning

Machine Learning intern

ISMRITI
juin 2018 - août 2018 (2 mois, 2 jours)
Developed scalable Natural language semantic text search engine and image hashing search engine. Also mentored a class of 400 students in an AI workshop conducted by ISMRITI.

Éducation

Dual degree

Indian Institute of Technology, Kanpur, India 2019 - 2020
(1 an)

Bachelors of technology

Indian Institute of Technology, Kanpur, India 2015 - 2019
(4 ans)

Qualifications

Best project award

Science and Technology Club, IIT Kanpur
2018
Awarded the best project award for student search app using face recognition by IIT Kanpur

Contactez Ujjwal K. concernant votre emploi

Connectez-vous pour discuter des détails via la messagerie.

Vérifications

Freelance préféré
Identité vérifiée
Paiement vérifié
Téléphone vérifié
E-mail vérifié
Connecté à Facebook

Certifications

preferredfreelancer-1.png Preferred Freelancer Program SLA 1 97%

Meilleures compétences

Python 48 Machine Learning (ML) 37 Deep Learning 31 MATLAB 31 Matlab and Mathematica 10

Parcourir les freelances similaires

Python Developers in India
Python Developers
Machine Learning Experts
Deep Learning Specialists

Parcourir les présentations similaires

Python
Machine Learning (ML)
Deep Learning
MATLAB
Utilisateur précédent
Utilisateur suivant
Invitation désormais envoyée !
Utilisateurs enregistrés Total des travaux publiés
Freelancer ® is a registered Trademark of Freelancer Technology Pty Limited (ACN 142 189 759)
Copyright © 2023 Freelancer Technology Pty Limited (ACN 142 189 759)
Chargement de l'aperçu
Permission donnée pour la géolocalisation.
Votre session de connexion a expiré et vous avez été déconnecté. Veuillez vous connecter à nouveau.