Lalit K. Profile | Freelancer

4.9

(26 reviews)

You're now following

Error following user.

This user does not allow users to follow them.

You are already following this user.

Your membership plan only allows 0 follows. Upgrade here.

Successfully unfollowed

Error unfollowing user.

You have successfully recommended

Error recommending user.

Something went wrong. Please refresh the page and try again.

Email successfully verified.

$20 USD / hour

mathura,

$20 USD / hour

It's currently 7:45 PM here

Joined July 30, 2017

3 Recommendations

Lalit K.

@lalitdeveloper7

4.9

(26 reviews)

3.9

3.9

54%

54%

$20 USD / hour

mathura,

$20 USD / hour

97%

Jobs Completed

96%

On Budget

90%

On Time

N/A

Repeat Hire Rate

Python|PHP|ML/DL|Data Science|App Development

My name is Lalit. I've been a Python and PHP developer for 7 years. I am providing following services: ◆ Python, OOP, Web development (Django, Flask, Streamlit)Machine Learning, Deep Learning, Computer Vision, Data Science ◆ Database Management, SQL, SQL Server, Postgres, Mongodb ◆ PHP, Web Development, Wordpress, Laravel ◆ Data Analysis, Data Visualization (Tableau, Power BI) ◆ Report Writing, Research writing ◆ Computer Security ◆ App Development (Flutter, React Native, Ionic Framework) I offer diverse skill services in assignments and projects, delivering quality work at affordable prices.

See all Python Developers in India

Contact Lalit K. about your job

Log in to discuss any details over chat.

Portfolio

11263788

This project showcases an accomplished dynamic, user-centered e-commerce websites. The focus is on leveraging Flask, MySQL databases, and front-end tools like HTML, CSS, and JavaScript to establish robust online shopping platforms. Flask's capabilities facilitate seamless management of user authentication, product categorization, dynamic search, and cart functionality, creating a fluid and immersive shopping experience. The project underscores the successful execution of vital features, encompassing user profiles, favorites, reviews, and secure transactions. The integration of technical prowess with a refined design sensibility yields impeccable shopping experiences, seamlessly harmonizing back-end functionality with front-end aesthetics.

Ecommerce Website using Flask framework

I have made several thesis, research, dissertation reports.

Report

I have made several thesis, research, dissertation reports.

Report

I have made several thesis, research, dissertation reports.

Report

I have made several thesis, research, dissertation reports.

Report

I designed a CNN-based diabetic retinopathy detection system with an impressive 98% accuracy. By leveraging deep convolutional neural networks, I created a robust model that identifies signs of diabetic retinopathy in retinal images. The project demonstrates my expertise in medical image analysis and deep learning, contributing to early disease diagnosis and patient care.

Diabetic Retinopathy Detection using CNN

I designed a CNN-based diabetic retinopathy detection system with an impressive 98% accuracy. By leveraging deep convolutional neural networks, I created a robust model that identifies signs of diabetic retinopathy in retinal images. The project demonstrates my expertise in medical image analysis and deep learning, contributing to early disease diagnosis and patient care.

Diabetic Retinopathy Detection using CNN

I designed a CNN-based diabetic retinopathy detection system with an impressive 98% accuracy. By leveraging deep convolutional neural networks, I created a robust model that identifies signs of diabetic retinopathy in retinal images. The project demonstrates my expertise in medical image analysis and deep learning, contributing to early disease diagnosis and patient care.

Diabetic Retinopathy Detection using CNN

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed a Medical Visual Question Answering (VQA) model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The model integrates image and text data to answer medical-related questions accurately. The project involves preprocessing medical images and text, extracting features using a pre-trained VGG16 CNN, and designing an LSTM-based question-answering architecture. The model was trained and evaluated on a large dataset, achieving substantial accuracy in predicting medical answers. This endeavor showcases the fusion of deep learning and medical knowledge to address real-world challenges in the healthcare domain.

Medical Visual Question Answering (VQA) model on CNN & LSTm

Developed a Medical Visual Question Answering (VQA) model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The model integrates image and text data to answer medical-related questions accurately. The project involves preprocessing medical images and text, extracting features using a pre-trained VGG16 CNN, and designing an LSTM-based question-answering architecture. The model was trained and evaluated on a large dataset, achieving substantial accuracy in predicting medical answers. This endeavor showcases the fusion of deep learning and medical knowledge to address real-world challenges in the healthcare domain.

Medical Visual Question Answering (VQA) model on CNN & LSTm

Developed a Medical Visual Question Answering (VQA) model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The model integrates image and text data to answer medical-related questions accurately. The project involves preprocessing medical images and text, extracting features using a pre-trained VGG16 CNN, and designing an LSTM-based question-answering architecture. The model was trained and evaluated on a large dataset, achieving substantial accuracy in predicting medical answers. This endeavor showcases the fusion of deep learning and medical knowledge to address real-world challenges in the healthcare domain.

Medical Visual Question Answering (VQA) model on CNN & LSTm

Developed a Medical Visual Question Answering (VQA) model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The model integrates image and text data to answer medical-related questions accurately. The project involves preprocessing medical images and text, extracting features using a pre-trained VGG16 CNN, and designing an LSTM-based question-answering architecture. The model was trained and evaluated on a large dataset, achieving substantial accuracy in predicting medical answers. This endeavor showcases the fusion of deep learning and medical knowledge to address real-world challenges in the healthcare domain.

Medical Visual Question Answering (VQA) model on CNN & LSTm

Developed a Medical Visual Question Answering (VQA) model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The model integrates image and text data to answer medical-related questions accurately. The project involves preprocessing medical images and text, extracting features using a pre-trained VGG16 CNN, and designing an LSTM-based question-answering architecture. The model was trained and evaluated on a large dataset, achieving substantial accuracy in predicting medical answers. This endeavor showcases the fusion of deep learning and medical knowledge to address real-world challenges in the healthcare domain.

Medical Visual Question Answering (VQA) model on CNN & LSTm

11263788

This project showcases an accomplished dynamic, user-centered e-commerce websites. The focus is on leveraging Flask, MySQL databases, and front-end tools like HTML, CSS, and JavaScript to establish robust online shopping platforms. Flask's capabilities facilitate seamless management of user authentication, product categorization, dynamic search, and cart functionality, creating a fluid and immersive shopping experience. The project underscores the successful execution of vital features, encompassing user profiles, favorites, reviews, and secure transactions. The integration of technical prowess with a refined design sensibility yields impeccable shopping experiences, seamlessly harmonizing back-end functionality with front-end aesthetics.

Ecommerce Website using Flask framework

I have made several thesis, research, dissertation reports.

Report

I have made several thesis, research, dissertation reports.

Report

I have made several thesis, research, dissertation reports.

Report

I have made several thesis, research, dissertation reports.

Report

I designed a CNN-based diabetic retinopathy detection system with an impressive 98% accuracy. By leveraging deep convolutional neural networks, I created a robust model that identifies signs of diabetic retinopathy in retinal images. The project demonstrates my expertise in medical image analysis and deep learning, contributing to early disease diagnosis and patient care.

Diabetic Retinopathy Detection using CNN

I designed a CNN-based diabetic retinopathy detection system with an impressive 98% accuracy. By leveraging deep convolutional neural networks, I created a robust model that identifies signs of diabetic retinopathy in retinal images. The project demonstrates my expertise in medical image analysis and deep learning, contributing to early disease diagnosis and patient care.

Diabetic Retinopathy Detection using CNN

I designed a CNN-based diabetic retinopathy detection system with an impressive 98% accuracy. By leveraging deep convolutional neural networks, I created a robust model that identifies signs of diabetic retinopathy in retinal images. The project demonstrates my expertise in medical image analysis and deep learning, contributing to early disease diagnosis and patient care.

Diabetic Retinopathy Detection using CNN

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

I developed an image classification project using Vision Transformer (ViT) and ResNet9 models for plant disease detection. Achieving 98% accuracy, the project effectively identifies various plant diseases. Leveraging one-cycle learning rate scheduling and visualization techniques, I ensured robust training and evaluation. The portfolio showcases my proficiency in deep learning and image analysis.

Plast Disease Detection using VIT and ResNet9

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed an energy prediction model for the UK using various machine learning techniques: Linear Regression, Decision Tree (DT), and Random Forest (RF). By analyzing historical energy data, we trained these models to forecast future energy consumption. Linear Regression identified linear trends, while DT and RF delved into complex patterns. The models' predictions were evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This project illustrates the application of machine learning in foreseeing energy demand, contributing to informed energy management decisions and sustainable practices.

Uk Energy Prediction using linear regression, DT, RF

Developed a Medical Visual Question Answering (VQA) model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The model integrates image and text data to answer medical-related questions accurately. The project involves preprocessing medical images and text, extracting features using a pre-trained VGG16 CNN, and designing an LSTM-based question-answering architecture. The model was trained and evaluated on a large dataset, achieving substantial accuracy in predicting medical answers. This endeavor showcases the fusion of deep learning and medical knowledge to address real-world challenges in the healthcare domain.

Medical Visual Question Answering (VQA) model on CNN & LSTm

Developed a Medical Visual Question Answering (VQA) model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The model integrates image and text data to answer medical-related questions accurately. The project involves preprocessing medical images and text, extracting features using a pre-trained VGG16 CNN, and designing an LSTM-based question-answering architecture. The model was trained and evaluated on a large dataset, achieving substantial accuracy in predicting medical answers. This endeavor showcases the fusion of deep learning and medical knowledge to address real-world challenges in the healthcare domain.

Medical Visual Question Answering (VQA) model on CNN & LSTm

Developed a Medical Visual Question Answering (VQA) model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The model integrates image and text data to answer medical-related questions accurately. The project involves preprocessing medical images and text, extracting features using a pre-trained VGG16 CNN, and designing an LSTM-based question-answering architecture. The model was trained and evaluated on a large dataset, achieving substantial accuracy in predicting medical answers. This endeavor showcases the fusion of deep learning and medical knowledge to address real-world challenges in the healthcare domain.

Medical Visual Question Answering (VQA) model on CNN & LSTm

Developed a Medical Visual Question Answering (VQA) model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The model integrates image and text data to answer medical-related questions accurately. The project involves preprocessing medical images and text, extracting features using a pre-trained VGG16 CNN, and designing an LSTM-based question-answering architecture. The model was trained and evaluated on a large dataset, achieving substantial accuracy in predicting medical answers. This endeavor showcases the fusion of deep learning and medical knowledge to address real-world challenges in the healthcare domain.

Medical Visual Question Answering (VQA) model on CNN & LSTm

Developed a Medical Visual Question Answering (VQA) model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The model integrates image and text data to answer medical-related questions accurately. The project involves preprocessing medical images and text, extracting features using a pre-trained VGG16 CNN, and designing an LSTM-based question-answering architecture. The model was trained and evaluated on a large dataset, achieving substantial accuracy in predicting medical answers. This endeavor showcases the fusion of deep learning and medical knowledge to address real-world challenges in the healthcare domain.

Medical Visual Question Answering (VQA) model on CNN & LSTm

Reviews

Changes saved

Showing 1 - 5 out of 26 reviews

Filter reviews by:

5.0

₹5,000.00 INR

Robust Table Detection with Machine Learning

great work . delivered project within time

Matlab and Mathematica

Machine Learning (ML)

C++ Programming

T

Thanu R.

@thanu41

•

21 hours ago

4.8

₹3,500.00 INR

Prediction model for mortality using six inflammatory markers as variables

Fast and efficient work

R Programming Language

Statistical Analysis

SPSS Statistics

M

Closed User

@Medicotrauma

•

7 months ago

5.0

₹3,900.00 INR

ABSA system using syntactic parsing -python

Great work on my ML model training in time. Great-clear communication. would love to work again.

Machine Learning (ML)

Hardik T.

@hardik88t

•

11 months ago

5.0

₹1,500.00 INR

netwrok graph clustering using canopy clustering

He is reliable and completes the work on time as per requirement. His humbleness is greatly appreciated.

Machine Learning (ML)

Sb V.

@bharatvanam34

•

1 year ago

5.0

₹1,600.00 INR

Recaptcha in python (Jupyter Notebook)

he is expertise and did wonderful work

Software Architecture

Jaswanth J.

@jaswanthv221

•

1 year ago

Experience

Codegiants

Apr 2019 - Jul 2022 (3 years, 3 months)

Have been worked as Machine Learning and Deep Learning Developer

Isprasoft Ptv Ltd.

Jun 2018 - Mar 2019 (9 months, 2 days)

Have worked as Laravel Framework web developer.

Iquincesoft Pvt. Ltd.

Jul 2017 - Jun 2018 (11 months, 1 day)

Have been worked as PHP Web Developer

Education

M.Tech

Pace University,

United States

2017 - 2019

(2 years)

B.Tech

Uttar Pradesh Technical University,

India

2012 - 2016

(4 years)

Qualifications

Edx.org

2019

Here I have completed the certification of Machine Learning Expert Level.

Contact Lalit K. about your job

Log in to discuss any details over chat.

Verifications

Preferred Freelancer

Identity Verified

Payment Verified

Phone Verified

Email Verified

Facebook Connected

Certifications

Preferred Freelancer Program SLA 1

97%

Top Skills

15

8

Software Architecture

7

6

Machine Learning (ML)

6

Browse Similar Freelancers

Python Developers in India

Python Developers

MySQL Developers

Software Architecture Developers

Browse Similar Showcases

Software Architecture

Invite sent successfully!

Thanks! We’ve emailed you a link to claim your free credit.

Something went wrong while sending your email. Please try again.

Loading preview

Permission granted for Geolocation.

Your login session has expired and you have been logged out. Please log in again.