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Akshat S.

@akshat26oct99

monthly-level-three.svgverified-v2.svgrising-star.svg
5,0
2
3,9
3,9
100%

❇️Machine Learning and AI Expert❇️

$20 USD / orë
・
Flamuri i
United Kingdom (3:36 p.d.)
・
U bashkua në nëntor 2, 2023
$20 USD / orë
・
I’m Akshat Sharma, the founder of [login to view URL], a startup revolutionizing businesses with Artificial Intelligence solutions. With 5+ years of experience as a Machine Learning Engineer, I specialize in delivering tailored AI solutions that drive success. I hold an MSc in Data Science from Queen Mary University of London and a BSc in Computer Science from SRM University. ➡️ What I Bring to the Table: ⚙️ AI & Machine Learning Expertise: •⚡Computer Vision: Object Detection, Image Segmentation, Face Recognition, 3D Reconstruction, Video Analytics. •⚡Natural Language Processing: Chatbot Development, Sentiment Analysis, Text Summarization, and Language Models. •⚡Predictive Analytics: Stock Price Prediction, Demand Forecasting, Fraud Detection, and Risk Analysis. ⚙️ Full-Stack Development Proficiency: •⚡Frontend Development: React.js, Next.js, Tailwind CSS, Responsive UIs. •⚡Backend Development: Node.js, Django, RESTful APIs. •⚡Database Management: MySQL, SQL, and NoSQL Solutions. ☁️ Cloud & Deployment: AWS & GCP for scalable AI and web application deployment. ✅ Why Work With Me? •⚡Proven track record of delivering scalable, high-quality solutions. •⚡Focused on clean, maintainable, and future-proof code. •⚡Committed to understanding your vision and exceeding expectations. Whether you need advanced AI models, custom web development, or predictive analytics, I’m ready to collaborate and bring your ideas to life! ✅ Let’s connect and create something exceptional!
Verifikimet
Në kohë
100%
Në buxhet
100%
Normë pranimi
100%
Përsëritni normën e punësimit
50%
Freelancer Mechanical Engineers United Kingdom
Shikoni të gjitha Mechanical Engineers në United Kingdom

Portofol

Portofol

12265384
Designed and developed VisionSEO, an AI-driven tool to evaluate SEO strength by analyzing screenshots and web elements such as search boxes, links, and text regions. Utilized YOLOv8 for web element detection and EasyOCR/Tesseract for text extraction. Deployed custom AI models on AWS (EC2, SageMaker) to enable real-time image processing and API integration for SEO evaluation. Developed an algorithm to compare user-entered text with detected web element text, delivering SEO performance insights based on similarity scores. Integrated backend with Flask and built the frontend interface using React, gaining hands-on experience with AI model deployment, optimization, and inference for web applications. This project was developed as part of my role at Smallcap.ai.
VisionSEO - AI-based SEO Strength Checker
12265375
Led the development of an AI-powered web platform using Gemmni Pro to analyze CVs and match candidates with job descriptions. Directed a cross-functional team, ensuring timely delivery and high client satisfaction. Oversaw the integration of Gemmni Pro for extracting key features, improving candidate-job matching. Managed secure storage of CVs and job descriptions, facilitating efficient data retrieval and analysis. Engaged with clients, guiding them through the platform and helping them upload databases. Monitored AI performance, establishing metrics and driving improvements based on feedback. Successfully launched the platform, resulting in a 30% increase in candidate placements in the first quarter, while enhancing client satisfaction through streamlined CV matching and retrieval processes, contributing to positive testimonials and repeat business.
CV Analysis Platform Project
11459023
Introduction This project aims to detect and classify traffic objects in real-time using two advanced models: YOLO and Faster R-CNN. We trained these models on the BDD100K dataset, a comprehensive driving video dataset with diverse scenes and annotations. The goal is to compare their performances and achieve a comparable mean Average Precision (mAP) to the state-of-the-art. Dataset The BDD100K dataset includes 100K driving videos with 13 object categories annotated with bounding boxes. The dataset provides a rich variety of geographic, environmental, and weather conditions. Model Weights YOLO: The YOLO model was implemented from scratch. Faster R-CNN: Model weights can be downloaded from the following link: [Link to Faster R-CNN model weights] Results The models were evaluated on the BDD100K test dataset using an NVIDIA V100 SXM2 32GB GPU: Architecture mAP (%) FPS YOLO 18.6 212.4 Faster R-CNN 41.8 17.1
Real Time Object Detection and Classification for Autonomous
11459081
In my pivotal role as a key contributor to the Geo Spatial Data Analytics project, I spearheaded an interdisciplinary endeavor that merged Machine Learning (ML) with Geospatial Analysis to study and predict natural disasters. By collecting and integrating extensive geospatial data, employing advanced algorithms, and utilizing predictive models like Random Forest and Decision Trees, we gained invaluable insights into the distribution, frequency, and impact of natural calamities. Through the development of interactive maps with Folium, our project empowered stakeholders and decision-makers to make informed choices in disaster management. This project exemplifies my expertise in geospatial data analysis and predictive modeling, highlighting its potential to save lives and resources in the face of natural disasters. If you're looking for a professional to implement similar solutions or elevate your projects, I'm here to help.
Predicting Natural Disasters with Geo Spatial Data Analytics
11459046
Our project delves into the realm of facial recognition technology, meticulously scrutinizing its gender biases. With a particular focus on the FaceNet algorithm, we map facial images to vectors, enhancing recognition capabilities and enabling a comprehensive analysis of gender biases. Through dual analyses, we evaluate FaceNet's performance on both genders using the LFW dataset and predict gender using the UTKfaces dataset, comparing results to alternative methods. Our project sheds light on the complex interplay between facial recognition technology and gender biases, contributing to ethical discussions surrounding these technologies. It paves the way for future research to rectify gender biases in facial recognition algorithms. Technologies employed include Python, TensorFlow, FaceNet, and neural networks, reflecting our commitment to addressing ethical and societal implications in this domain. For expertise or collaborative opportunities in this critical area, please reach out.
Face Detection Project
12265384
Designed and developed VisionSEO, an AI-driven tool to evaluate SEO strength by analyzing screenshots and web elements such as search boxes, links, and text regions. Utilized YOLOv8 for web element detection and EasyOCR/Tesseract for text extraction. Deployed custom AI models on AWS (EC2, SageMaker) to enable real-time image processing and API integration for SEO evaluation. Developed an algorithm to compare user-entered text with detected web element text, delivering SEO performance insights based on similarity scores. Integrated backend with Flask and built the frontend interface using React, gaining hands-on experience with AI model deployment, optimization, and inference for web applications. This project was developed as part of my role at Smallcap.ai.
VisionSEO - AI-based SEO Strength Checker
12265375
Led the development of an AI-powered web platform using Gemmni Pro to analyze CVs and match candidates with job descriptions. Directed a cross-functional team, ensuring timely delivery and high client satisfaction. Oversaw the integration of Gemmni Pro for extracting key features, improving candidate-job matching. Managed secure storage of CVs and job descriptions, facilitating efficient data retrieval and analysis. Engaged with clients, guiding them through the platform and helping them upload databases. Monitored AI performance, establishing metrics and driving improvements based on feedback. Successfully launched the platform, resulting in a 30% increase in candidate placements in the first quarter, while enhancing client satisfaction through streamlined CV matching and retrieval processes, contributing to positive testimonials and repeat business.
CV Analysis Platform Project
11459023
Introduction This project aims to detect and classify traffic objects in real-time using two advanced models: YOLO and Faster R-CNN. We trained these models on the BDD100K dataset, a comprehensive driving video dataset with diverse scenes and annotations. The goal is to compare their performances and achieve a comparable mean Average Precision (mAP) to the state-of-the-art. Dataset The BDD100K dataset includes 100K driving videos with 13 object categories annotated with bounding boxes. The dataset provides a rich variety of geographic, environmental, and weather conditions. Model Weights YOLO: The YOLO model was implemented from scratch. Faster R-CNN: Model weights can be downloaded from the following link: [Link to Faster R-CNN model weights] Results The models were evaluated on the BDD100K test dataset using an NVIDIA V100 SXM2 32GB GPU: Architecture mAP (%) FPS YOLO 18.6 212.4 Faster R-CNN 41.8 17.1
Real Time Object Detection and Classification for Autonomous
11459081
In my pivotal role as a key contributor to the Geo Spatial Data Analytics project, I spearheaded an interdisciplinary endeavor that merged Machine Learning (ML) with Geospatial Analysis to study and predict natural disasters. By collecting and integrating extensive geospatial data, employing advanced algorithms, and utilizing predictive models like Random Forest and Decision Trees, we gained invaluable insights into the distribution, frequency, and impact of natural calamities. Through the development of interactive maps with Folium, our project empowered stakeholders and decision-makers to make informed choices in disaster management. This project exemplifies my expertise in geospatial data analysis and predictive modeling, highlighting its potential to save lives and resources in the face of natural disasters. If you're looking for a professional to implement similar solutions or elevate your projects, I'm here to help.
Predicting Natural Disasters with Geo Spatial Data Analytics
11459046
Our project delves into the realm of facial recognition technology, meticulously scrutinizing its gender biases. With a particular focus on the FaceNet algorithm, we map facial images to vectors, enhancing recognition capabilities and enabling a comprehensive analysis of gender biases. Through dual analyses, we evaluate FaceNet's performance on both genders using the LFW dataset and predict gender using the UTKfaces dataset, comparing results to alternative methods. Our project sheds light on the complex interplay between facial recognition technology and gender biases, contributing to ethical discussions surrounding these technologies. It paves the way for future research to rectify gender biases in facial recognition algorithms. Technologies employed include Python, TensorFlow, FaceNet, and neural networks, reflecting our commitment to addressing ethical and societal implications in this domain. For expertise or collaborative opportunities in this critical area, please reach out.
Face Detection Project
Ndryshimet u ruajtën
5.0 · 2 Reviews
Përshtypje
Z
BlazeX website • $466 USD
I highly recommend this freelancer! Working with this professional was an absolute pleasure. Not only did he complete every task on time, but he also exceeded all expectations. The quality of his work was exceptional, with meticulous attention to detail and a clear understanding of the project. Communication was always prompt and clear, and he was open to discussing ideas, making adjustments, and proposing improvements. I’ll definitely work with him again. Highly recommended! ?
Danyil K.
@Z131213
•
Flamuri i
Cherkasy, Ukraine
•
14 days ago
Shpërndaj
Z
Трейдинг бот в телеграме • $400 USD
Цей фрілансер чудовий!
Danyil K.
@Z131213
•
Flamuri i
Cherkasy, Ukraine
•
1 month ago
Shpërndaj
Eksperienca
AI Engineer
sht, 2021 - sht, 2024
•
3 years
Smallcap.ai
sht, 2021 - sht, 2024
•
3 years
* Led the CV Analysis Platform project using Gemmni Pro, boosting candidate-job placements by 30%. * Developed VisionSEO, a tool for SEO strength assessment, utilizing YOLOv8 and OCR for real-time image processing on AWS. * Created an AI text tagging system with SpaCy, enhancing accuracy by 30% through dynamic UI integration and Flask backend management. * Focused on leadership, AI integration, and optimizing user experiences with innovative web solutions.
Flamuri i
London, United Kingdom
sht, 2021 - sht, 2024
•
3 years
Junior Python Developer
pri, 2019 - pri, 2021
•
2 years
TCPL
pri, 2019 - pri, 2021
•
2 years
* Developed data extraction projects using Python for efficient information gathering. * Gained expertise in data manipulation, cleaning, and preprocessing. * Collaborated with data scientists on AI projects, deploying cutting-edge solutions using Python libraries. * Contributed to code reviews, quality assurance, and best practices.
Flamuri i
Delhi, India
pri, 2019 - pri, 2021
•
2 years
Arsimi
Queen Mary, U. of London
2023 - 2025
•
2 years
PhD. in AI
Flamuri i
United Kingdom
2023 - 2025
•
2 years
Queen Mary, U. of London
2022 - 2023
•
1 year
MSc. in Data Analytics
Flamuri i
United Kingdom
2022 - 2023
•
1 year
SRM University
2018 - 2022
•
4 years
BSc. Computer Science
Flamuri i
India
2018 - 2022
•
4 years
Kualifikimet
Python For Data Science, AI & Development
2021
Coursera
> Explore the fundamental concepts of Python for Data Science and AI, diving into Neural Networks (NN), Computer Vision, and Deep Learning (DL) to understand their practical applications and build real-world solutions. > Gain hands-on experience in developing AI projects, from image recognition to natural language processing, while mastering the Python programming language and its libraries for data analysis and machine learning.
2021
Introduction To Data Science: Statistical Programming With R
2020
Coursera
> Learn statistical programming with R, a powerful tool for data analysis and visualization, providing essential skills for data scientists and analysts. > Gain expertise in key data science concepts and techniques, including data manipulation, statistical analysis, and the creation of data visualizations to extract insights from datasets.
2020
Deep Learning Using Python
2020
Coursera
> Master the fundamentals of deep learning techniques and applications using Python, including neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). > Gain hands-on experience in building and training deep learning models for image recognition, natural language processing, and other real-world applications, equipping you with the skills to tackle complex AI projects.
2020
Dёshmitё
P
Preferred Freelancer Program SLA
US English - Level 1
US English
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