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$20 USD / giờ
Cờ của INDIA
mathura, india
$20 USD / giờ
Hiện ở đây đang là 2:22 CH
Đã tham gia vào tháng 7 30, 2017
3 Khuyến nghị

Lalit K.

@lalitdeveloper7

 4,9 (25 nhận xét)
3,9
3,9
53%
53%
$20 USD / giờ
Cờ của INDIA
mathura, india
$20 USD / giờ
96%
Công Việc Hoàn Thành
96%
Đúng Ngân Sách
89%
Đúng Thời Hạn
Không áp dụng
Tỉ Lệ Thuê Lại

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.
Freelancer Python Developers India
Xem tất cả về Python Developers tại India

Liên hệ Lalit K. về công việc của bạn

Đăng nhập để thảo luận thông tin thông qua 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
11257962
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
11257921
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
11257904
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
11257885
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
11257875
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
11257962
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
11257921
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
11257904
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
11257885
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
11257875
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

Nhận xét

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Hiện 1 - 5 trong số 25 nhận xét
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4,8
₹3.500,00 INR
Prediction model for mortality using six inflammatory markers as variables
Fast and efficient work
Python
Statistics
R Programming Language
Statistical Analysis
SPSS Statistics
M
Closed User
@Medicotrauma
•
6 tháng trước
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.
Python
Machine Learning (ML)
Coding
Avatar người dùng
Cờ của Hardik T.
@hardik88t
•
10 tháng trước
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.
Python
Machine Learning (ML)
Avatar người dùng
Cờ của Sb V.
@bharatvanam34
•
1 năm trước
5,0
₹1.600,00 INR
Recaptcha in python (Jupyter Notebook)
he is expertise and did wonderful work
JavaScript
Python
Software Architecture
Avatar người dùng
Cờ của Jaswanth J.
@jaswanthv221
•
1 năm trước
5,0
₹2.500,00 INR
Python rembg api setup and host in server (azure)
Lalit is a highly skilled and dedicated Python developer. He has a deep understanding of the framework and is able to effectively utilize its various components to create robust and efficient applications. He has strong command over Python and can get the job done with minimal inputs. He understood the project requirements very quickly.
PHP
Python
Linux
Software Architecture
S
Closed User
@sricalmradio
•
1 năm trước

Kinh nghiệm

Python ML Developer

Codegiants
thg 4 2019 - thg 7 2022 (3 năm, 3 tháng)
Have been worked as Machine Learning and Deep Learning Developer

Web Developer

Isprasoft Ptv Ltd.
thg 6 2018 - thg 3 2019 (9 tháng, 2 ngày)
Have worked as Laravel Framework web developer.

Web Developer

Iquincesoft Pvt. Ltd.
thg 7 2017 - thg 6 2018 (11 tháng, 1 ngày)
Have been worked as PHP Web Developer

Học vấn

M.Tech

Pace University, United States 2017 - 2019
(2 năm)

B.Tech

Uttar Pradesh Technical University, India 2012 - 2016
(4 năm)

Bằng cấp

Python Developer

Edx.org
2019
Here I have completed the certification of Machine Learning Expert Level.

Liên hệ Lalit K. về công việc của bạn

Đăng nhập để thảo luận thông tin thông qua chat.

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MySQL 8
Software Architecture 7
Machine Learning (ML) 5
HTML 4

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