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Artificial Intelligence
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OCR
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$4 USD / hora
Bandera de PAKISTAN
faisalabad, pakistan
$4 USD / hora
Aquí son las 6:44 p. m.
Se unió el enero 18, 2020
4 recomendaciones

Abdul R.

@MiniDeveloper21

monthly-level-three.svg 5,0 (73 comentarios)
5,0
5,0
75%
75%
$4 USD / hora
Bandera de PAKISTAN
faisalabad, pakistan
$4 USD / hora
99 %
Trabajos finalizados
95 %
Dentro del presupuesto
97 %
A tiempo
15 %
Tasa de recontratación

Data Scientist | Python Programmer | Web Scraper

Hey there! I'm a seasoned Data Scientist with 3+ years of experience in various AI fields, including Computer Vision, NLP, Deep Learning, and Machine Learning. I'm dedicated to creating impactful solutions that drive transformative results. I'm an active contributor on popular freelancing platforms, where I take on diverse projects and deliver innovative AI solutions. My expertise includes: ➤ Web Scraping ➤ Chatbots and Voice Agents ➤ Object Detection ➤ Object Tracking / Counting ➤ LLM (Language Modeling and Learning) Fine tuning ➤ Machine learning Model Development ➤ API Creation (Flask API /Fast API) ➤ OCR Invoice Parsing ➤ Model Deployment ➤ GANS I'm passionate about the entire AI development lifecycle, from model development and deployment to API creation. I take pride in my commitment to excellence and attention to detail, ensuring that every project meets the highest standards. With experience in various AI tools and technologies, I'm able to tackle complex problems and deliver solutions that exceed expectations. Whether it's building custom chatbots or developing sophisticated object detection models, I'm committed to driving value and delivering results.
Freelancer Python Developers Pakistan
Ver todo Python Developers en Pakistan

Contacta Abdul R. sobre tu trabajo

Inicia sesión para comentar cualquier detalle por chat.

Portafolio

8568057
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
11840712
The provided repository includes PyTorch code for detecting cracks on concrete surfaces. The implementation is based on the DeepCrack model, which utilizes Convolutional Neural Networks (CNNs) for crack damage detection. DeepCrack employs a deep hierarchical feature learning architecture specifically designed for crack segmentation
Crack Detection & Segmentation
11840701
Professional editors manually crops the interesting events that occurs in a match and joins in sequence to make cricket highlights. Each year many cricket matches are recorded and the viewers wants quick updates of the match by watching highlights. Simple Genetic Algorithm Approach: 1. Generating Initial Population 2. Setting Up Fitness Function 3. Making Probability List 4. Random Selection (Roulette Wheel Selection) 5. Performing Crossover 6. Performing Mutation
Cricket Highlights Extraction
11840708
In this project, we aim to develop a robust system for person detection, tracking, and counting using state-of-the-art computer vision algorithms. Leveraging the power of YOLO (You Only Look Once) for accurate object detection and DeepSORT (Deep Simple Online and Realtime Tracking) for person tracking, our system will analyze video footage to identify and monitor individuals throughout the sequence.
Person Detection & Tracking
11840420
In this innovative project, I leverage the power of computer vision to precisely measure the angles of the elbow joints in human arms. The foundation of our solution lies in the integration of the OpenPose model, a cutting-edge technology capable of accurately detecting and tracking key body joints from images or video streams.
Elbow-Angles-Detection-Using-Pose-Estimation
6120193
Scraping the records of football stats and safe it in excel files......
Web Scraping(Text Extraction and Excel File Savage)
8568057
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
11840712
The provided repository includes PyTorch code for detecting cracks on concrete surfaces. The implementation is based on the DeepCrack model, which utilizes Convolutional Neural Networks (CNNs) for crack damage detection. DeepCrack employs a deep hierarchical feature learning architecture specifically designed for crack segmentation
Crack Detection & Segmentation
11840701
Professional editors manually crops the interesting events that occurs in a match and joins in sequence to make cricket highlights. Each year many cricket matches are recorded and the viewers wants quick updates of the match by watching highlights. Simple Genetic Algorithm Approach: 1. Generating Initial Population 2. Setting Up Fitness Function 3. Making Probability List 4. Random Selection (Roulette Wheel Selection) 5. Performing Crossover 6. Performing Mutation
Cricket Highlights Extraction
11840708
In this project, we aim to develop a robust system for person detection, tracking, and counting using state-of-the-art computer vision algorithms. Leveraging the power of YOLO (You Only Look Once) for accurate object detection and DeepSORT (Deep Simple Online and Realtime Tracking) for person tracking, our system will analyze video footage to identify and monitor individuals throughout the sequence.
Person Detection & Tracking
11840420
In this innovative project, I leverage the power of computer vision to precisely measure the angles of the elbow joints in human arms. The foundation of our solution lies in the integration of the OpenPose model, a cutting-edge technology capable of accurately detecting and tracking key body joints from images or video streams.
Elbow-Angles-Detection-Using-Pose-Estimation
6120193
Scraping the records of football stats and safe it in excel files......
Web Scraping(Text Extraction and Excel File Savage)

Comentarios

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5,0
$90,00 USD
Python Script for Data Input Automation
Great Freelancer - Very friendly & capable of the work. Defo recommend!
PHP
Python
Linux
Web Scraping
Software Architecture
W
Bandera de Harrison Terence W.
@Wynne01
•
hace 17 días
5,0
$150,00 USD
Create web scrapping program
He was very helpful and helped to provide an adequate solution to my problem
PHP
.NET
Web Scraping
C# Programming
Software Architecture
Avatar del usuario
Bandera de Sebastián H.
@sebastianhudson
•
hace 2 años
5,0
$25,00 USD
Help me run this python script
Willing to share screens and help whenever needed! Great work
PHP
JavaScript
Python
Software Architecture
Avatar del usuario
Bandera de Alphabcd C.
@AlpaBCD
•
hace 2 años
5,0
$135,00 AUD
Project for Abdul R.
Abdul is very polite. Good at work. Explained project really well.!! Can count on his work.
Java
Mobile App Development
Android
SQL
+1 más
Avatar del usuario
Bandera de Vinay Krishna B.
@krishnavinay87
•
hace 3 años
5,0
$40,00 AUD
Project 31624410 has been deleted
Delivered project before agreed date. very quick at work very responsive. Communication is excellent. will definitely work with him again. one can definitely count on his work. thank you so much for being kind and patience. Abdul.
Avatar del usuario
Bandera de Vinay Krishna B.
@krishnavinay87
•
hace 3 años

Experiencia

Data Scientist

Sparkix Technologies
feb 2023 - Presente
* Develop and implement data models to extract insights from large and complex datasets * Collaborate with cross-functional teams to identify business problems and determine how data can be used to solve them * Clean, preprocess, and wrangle data to prepare it for analysis and modeling * Communicate findings and recommendations to stakeholders through visualizations, reports, and presentations * Stay up-to-date with the latest industry trends and advancements in data science techniques and tools

Data Scientist

Machine Learning 1
oct 2021 - feb 2023 (1 año, 4 meses)
➤ Analyzing large datasets to extract meaningful insights and trends ➤ Developing and implementing machine learning models for predictive analytics ➤ Creating and validating hypotheses to guide data-driven decision-making ➤ Visualizing data and results to communicate findings to non-technical stakeholders ➤ Implementing data science solutions in a scalable and reproducible manner ➤ Developing and deploying machine learning models into production environments

Python Web Scraper

Eziline
may 2021 - ago 2021 (3 meses, 1 día)
➤ Developing web scraping scripts or tools to extract data from websites. ➤ Implementing proxy rotation and anti-bot measures to avoid detection. ➤ Handling and troubleshooting issues related to website access and data extraction. ➤ Managing and optimizing the storage and organization of scraped data. ➤ Documenting the scraping process, methodologies, and any challenges faced.

Educación

Bachelors in Software Engineering

COMSATS Institute of Information Technology, Pakistan 2016 - 2020
(4 años)

Calificaciones

Full Stack Data Scientist

iNeuron
2021
➤ Data Collection and Cleaning ➤ Database Management ➤ Data Analysis and Modeling ➤ Data Visualization ➤ Web Development ➤ APIs and Microservices ➤ Cloud Computing ➤ Machine Learning and Predictive Analytics ➤ Statistical Analysis ➤ Programming languages (e.g., Python) ➤ Version Control (e.g., Git) ➤ Software Development Life Cycle (SDLC) ➤ Deployment and Integration of Models ➤ Data Security and Privacy Measures ➤ Continuous Learning and Keeping Up with Industry Trends

Contacta Abdul R. sobre tu trabajo

Inicia sesión para comentar cualquier detalle por chat.

Verificaciones

Freelancer preferente
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Email verificado
Facebook conectado

Certificaciones

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

Principales habilidades

Python 8
Web Scraping 4
Nginx
OCR
Artificial Intelligence

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