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Kashan B.

@KashanBaig1

0.0
0
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Machine Learning | Deep Learning | AI

$20 USD / Hour
・
Flag of
Pakistan (1:31 PM)
・
Joined on November 6, 2024
$20 USD / Hour
・
As an experienced machine learning engineer with a passion for image classification and deep learning, I specialize in developing intelligent systems that can analyze and understand visual data. My expertise spans across various frameworks like TensorFlow, Keras, and PyTorch, allowing me to design and deploy robust models tailored to your needs. Whether you require building a custom image classifier, enhancing model performance, or integrating AI into your existing workflows, I have the skills to deliver top-quality solutions. I excel in data preprocessing, model training, and evaluation, ensuring every project is optimized for accuracy and efficiency. With a keen eye for detail and a dedication to staying up-to-date with the latest advancements in AI, I can help bring your ideas to life, transforming complex visual data into actionable insights. Let’s collaborate to create powerful solutions that push the boundaries of image recognition and deep learning!
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Portfolio

Portfolio

12424429
Project Overview: Objective: The goal of the project is to develop a system that can recognize and extract housing numbers (digits) from street view images. This can be used in applications such as automated address recognition, navigation systems, or for processing images in postal or delivery services. Tools and Technologies Used: Python: Python was the primary language used for the project, providing a rich ecosystem of libraries for image processing and machine learning. Machine Learning Models: You likely used Convolutional Neural Networks (CNNs), a type of deep learning model that excels in image classification tasks. CNNs can identify patterns in images and classify digits (0-9) in street view images. OpenCV: This library was used for image pre-processing, such as resizing, thresholding, and extracting regions of interest (e.g., the housing numbers from the street view images). TensorFlow / Keras: These deep learning frameworks were likely used for building and training the m
street-view-housing-number-digit-rec
Project Overview: Objective: The goal of the project is to develop a system that can recognize and extract housing numbers (digits) from street view images. This can be used in applications such as automated address recognition, navigation systems, or for processing images in postal or delivery services. Tools and Technologies Used: Python: Python was the primary language used for the project, providing a rich ecosystem of libraries for image processing and machine learning. Machine Learning Models: You likely used Convolutional Neural Networks (CNNs), a type of deep learning model that excels in image classification tasks. CNNs can identify patterns in images and classify digits (0-9) in street view images. OpenCV: This library was used for image pre-processing, such as resizing, thresholding, and extracting regions of interest (e.g., the housing numbers from the street view images). TensorFlow / Keras: These deep learning frameworks were likely used for building and training the m
street-view-housing-number-digit-rec
Project Overview: Objective: The goal of the project is to develop a system that can recognize and extract housing numbers (digits) from street view images. This can be used in applications such as automated address recognition, navigation systems, or for processing images in postal or delivery services. Tools and Technologies Used: Python: Python was the primary language used for the project, providing a rich ecosystem of libraries for image processing and machine learning. Machine Learning Models: You likely used Convolutional Neural Networks (CNNs), a type of deep learning model that excels in image classification tasks. CNNs can identify patterns in images and classify digits (0-9) in street view images. OpenCV: This library was used for image pre-processing, such as resizing, thresholding, and extracting regions of interest (e.g., the housing numbers from the street view images). TensorFlow / Keras: These deep learning frameworks were likely used for building and training the m
street-view-housing-number-digit-rec
Project Overview: Objective: The goal of the project is to develop a system that can recognize and extract housing numbers (digits) from street view images. This can be used in applications such as automated address recognition, navigation systems, or for processing images in postal or delivery services. Tools and Technologies Used: Python: Python was the primary language used for the project, providing a rich ecosystem of libraries for image processing and machine learning. Machine Learning Models: You likely used Convolutional Neural Networks (CNNs), a type of deep learning model that excels in image classification tasks. CNNs can identify patterns in images and classify digits (0-9) in street view images. OpenCV: This library was used for image pre-processing, such as resizing, thresholding, and extracting regions of interest (e.g., the housing numbers from the street view images). TensorFlow / Keras: These deep learning frameworks were likely used for building and training the m
street-view-housing-number-digit-rec
Project Overview: Objective: The goal of the project is to develop a system that can recognize and extract housing numbers (digits) from street view images. This can be used in applications such as automated address recognition, navigation systems, or for processing images in postal or delivery services. Tools and Technologies Used: Python: Python was the primary language used for the project, providing a rich ecosystem of libraries for image processing and machine learning. Machine Learning Models: You likely used Convolutional Neural Networks (CNNs), a type of deep learning model that excels in image classification tasks. CNNs can identify patterns in images and classify digits (0-9) in street view images. OpenCV: This library was used for image pre-processing, such as resizing, thresholding, and extracting regions of interest (e.g., the housing numbers from the street view images). TensorFlow / Keras: These deep learning frameworks were likely used for building and training the m
street-view-housing-number-digit-rec
12424389
Project Overview: Objective: The aim of your project is to visualize the world population distribution using various graphical representations like bar charts, line graphs, or interactive maps. By doing so, you highlight trends, compare population growth across countries, and visualize demographic changes on a global scale. Tools and Technologies Used: Google Colab: Google Colab was used as the development environment. It allows you to write and execute Python code in an interactive notebook, making it ideal for data analysis and visualization tasks. Python Libraries: Pandas: Used for data manipulation and cleaning. It helps in handling large datasets and preparing them for analysis. Matplotlib / Seaborn: These libraries were likely used for creating static, high-quality visualizations, such as line charts, bar charts, or pie charts to represent the population data. Plotly / Folium (optional): If used, these libraries enable the creation of interactive visualizations, such as interac
Visualization of World Population
Project Overview: Objective: The aim of your project is to visualize the world population distribution using various graphical representations like bar charts, line graphs, or interactive maps. By doing so, you highlight trends, compare population growth across countries, and visualize demographic changes on a global scale. Tools and Technologies Used: Google Colab: Google Colab was used as the development environment. It allows you to write and execute Python code in an interactive notebook, making it ideal for data analysis and visualization tasks. Python Libraries: Pandas: Used for data manipulation and cleaning. It helps in handling large datasets and preparing them for analysis. Matplotlib / Seaborn: These libraries were likely used for creating static, high-quality visualizations, such as line charts, bar charts, or pie charts to represent the population data. Plotly / Folium (optional): If used, these libraries enable the creation of interactive visualizations, such as interac
Visualization of World Population
Project Overview: Objective: The aim of your project is to visualize the world population distribution using various graphical representations like bar charts, line graphs, or interactive maps. By doing so, you highlight trends, compare population growth across countries, and visualize demographic changes on a global scale. Tools and Technologies Used: Google Colab: Google Colab was used as the development environment. It allows you to write and execute Python code in an interactive notebook, making it ideal for data analysis and visualization tasks. Python Libraries: Pandas: Used for data manipulation and cleaning. It helps in handling large datasets and preparing them for analysis. Matplotlib / Seaborn: These libraries were likely used for creating static, high-quality visualizations, such as line charts, bar charts, or pie charts to represent the population data. Plotly / Folium (optional): If used, these libraries enable the creation of interactive visualizations, such as interac
Visualization of World Population
Visualization of World Population
12424369
Project Overview: Objective: The goal of your project is to detect and classify various diseases affecting tomato leaves. This can help farmers monitor the health of their crops and take preventive measures. Tools and Technologies Used: Roboflow: You used Roboflow, a platform that helps create and manage datasets for computer vision tasks. It simplifies the process of uploading, labeling, and augmenting images, enabling efficient training of your classification model. Python: Python was the primary programming language for your project, used for building and training the model, data preprocessing, and managing the entire pipeline. Model Training: You trained an image classification model using the dataset of tomato leaf images, labeled with various disease categories. The model learns to classify leaf diseases based on visual patterns in the images. Image Classification: The heart of your project involves image classification, where the model assigns a label (disease type or healthy
Tomato Leaf Disease Classification
Project Overview: Objective: The goal of your project is to detect and classify various diseases affecting tomato leaves. This can help farmers monitor the health of their crops and take preventive measures. Tools and Technologies Used: Roboflow: You used Roboflow, a platform that helps create and manage datasets for computer vision tasks. It simplifies the process of uploading, labeling, and augmenting images, enabling efficient training of your classification model. Python: Python was the primary programming language for your project, used for building and training the model, data preprocessing, and managing the entire pipeline. Model Training: You trained an image classification model using the dataset of tomato leaf images, labeled with various disease categories. The model learns to classify leaf diseases based on visual patterns in the images. Image Classification: The heart of your project involves image classification, where the model assigns a label (disease type or healthy
Tomato Leaf Disease Classification
Project Overview: Objective: The goal of your project is to detect and classify various diseases affecting tomato leaves. This can help farmers monitor the health of their crops and take preventive measures. Tools and Technologies Used: Roboflow: You used Roboflow, a platform that helps create and manage datasets for computer vision tasks. It simplifies the process of uploading, labeling, and augmenting images, enabling efficient training of your classification model. Python: Python was the primary programming language for your project, used for building and training the model, data preprocessing, and managing the entire pipeline. Model Training: You trained an image classification model using the dataset of tomato leaf images, labeled with various disease categories. The model learns to classify leaf diseases based on visual patterns in the images. Image Classification: The heart of your project involves image classification, where the model assigns a label (disease type or healthy
Tomato Leaf Disease Classification
Tomato Leaf Disease Classification
12424429
Project Overview: Objective: The goal of the project is to develop a system that can recognize and extract housing numbers (digits) from street view images. This can be used in applications such as automated address recognition, navigation systems, or for processing images in postal or delivery services. Tools and Technologies Used: Python: Python was the primary language used for the project, providing a rich ecosystem of libraries for image processing and machine learning. Machine Learning Models: You likely used Convolutional Neural Networks (CNNs), a type of deep learning model that excels in image classification tasks. CNNs can identify patterns in images and classify digits (0-9) in street view images. OpenCV: This library was used for image pre-processing, such as resizing, thresholding, and extracting regions of interest (e.g., the housing numbers from the street view images). TensorFlow / Keras: These deep learning frameworks were likely used for building and training the m
street-view-housing-number-digit-rec
Project Overview: Objective: The goal of the project is to develop a system that can recognize and extract housing numbers (digits) from street view images. This can be used in applications such as automated address recognition, navigation systems, or for processing images in postal or delivery services. Tools and Technologies Used: Python: Python was the primary language used for the project, providing a rich ecosystem of libraries for image processing and machine learning. Machine Learning Models: You likely used Convolutional Neural Networks (CNNs), a type of deep learning model that excels in image classification tasks. CNNs can identify patterns in images and classify digits (0-9) in street view images. OpenCV: This library was used for image pre-processing, such as resizing, thresholding, and extracting regions of interest (e.g., the housing numbers from the street view images). TensorFlow / Keras: These deep learning frameworks were likely used for building and training the m
street-view-housing-number-digit-rec
Project Overview: Objective: The goal of the project is to develop a system that can recognize and extract housing numbers (digits) from street view images. This can be used in applications such as automated address recognition, navigation systems, or for processing images in postal or delivery services. Tools and Technologies Used: Python: Python was the primary language used for the project, providing a rich ecosystem of libraries for image processing and machine learning. Machine Learning Models: You likely used Convolutional Neural Networks (CNNs), a type of deep learning model that excels in image classification tasks. CNNs can identify patterns in images and classify digits (0-9) in street view images. OpenCV: This library was used for image pre-processing, such as resizing, thresholding, and extracting regions of interest (e.g., the housing numbers from the street view images). TensorFlow / Keras: These deep learning frameworks were likely used for building and training the m
street-view-housing-number-digit-rec
Project Overview: Objective: The goal of the project is to develop a system that can recognize and extract housing numbers (digits) from street view images. This can be used in applications such as automated address recognition, navigation systems, or for processing images in postal or delivery services. Tools and Technologies Used: Python: Python was the primary language used for the project, providing a rich ecosystem of libraries for image processing and machine learning. Machine Learning Models: You likely used Convolutional Neural Networks (CNNs), a type of deep learning model that excels in image classification tasks. CNNs can identify patterns in images and classify digits (0-9) in street view images. OpenCV: This library was used for image pre-processing, such as resizing, thresholding, and extracting regions of interest (e.g., the housing numbers from the street view images). TensorFlow / Keras: These deep learning frameworks were likely used for building and training the m
street-view-housing-number-digit-rec
Project Overview: Objective: The goal of the project is to develop a system that can recognize and extract housing numbers (digits) from street view images. This can be used in applications such as automated address recognition, navigation systems, or for processing images in postal or delivery services. Tools and Technologies Used: Python: Python was the primary language used for the project, providing a rich ecosystem of libraries for image processing and machine learning. Machine Learning Models: You likely used Convolutional Neural Networks (CNNs), a type of deep learning model that excels in image classification tasks. CNNs can identify patterns in images and classify digits (0-9) in street view images. OpenCV: This library was used for image pre-processing, such as resizing, thresholding, and extracting regions of interest (e.g., the housing numbers from the street view images). TensorFlow / Keras: These deep learning frameworks were likely used for building and training the m
street-view-housing-number-digit-rec
12424389
Project Overview: Objective: The aim of your project is to visualize the world population distribution using various graphical representations like bar charts, line graphs, or interactive maps. By doing so, you highlight trends, compare population growth across countries, and visualize demographic changes on a global scale. Tools and Technologies Used: Google Colab: Google Colab was used as the development environment. It allows you to write and execute Python code in an interactive notebook, making it ideal for data analysis and visualization tasks. Python Libraries: Pandas: Used for data manipulation and cleaning. It helps in handling large datasets and preparing them for analysis. Matplotlib / Seaborn: These libraries were likely used for creating static, high-quality visualizations, such as line charts, bar charts, or pie charts to represent the population data. Plotly / Folium (optional): If used, these libraries enable the creation of interactive visualizations, such as interac
Visualization of World Population
Project Overview: Objective: The aim of your project is to visualize the world population distribution using various graphical representations like bar charts, line graphs, or interactive maps. By doing so, you highlight trends, compare population growth across countries, and visualize demographic changes on a global scale. Tools and Technologies Used: Google Colab: Google Colab was used as the development environment. It allows you to write and execute Python code in an interactive notebook, making it ideal for data analysis and visualization tasks. Python Libraries: Pandas: Used for data manipulation and cleaning. It helps in handling large datasets and preparing them for analysis. Matplotlib / Seaborn: These libraries were likely used for creating static, high-quality visualizations, such as line charts, bar charts, or pie charts to represent the population data. Plotly / Folium (optional): If used, these libraries enable the creation of interactive visualizations, such as interac
Visualization of World Population
Project Overview: Objective: The aim of your project is to visualize the world population distribution using various graphical representations like bar charts, line graphs, or interactive maps. By doing so, you highlight trends, compare population growth across countries, and visualize demographic changes on a global scale. Tools and Technologies Used: Google Colab: Google Colab was used as the development environment. It allows you to write and execute Python code in an interactive notebook, making it ideal for data analysis and visualization tasks. Python Libraries: Pandas: Used for data manipulation and cleaning. It helps in handling large datasets and preparing them for analysis. Matplotlib / Seaborn: These libraries were likely used for creating static, high-quality visualizations, such as line charts, bar charts, or pie charts to represent the population data. Plotly / Folium (optional): If used, these libraries enable the creation of interactive visualizations, such as interac
Visualization of World Population
Visualization of World Population
12424369
Project Overview: Objective: The goal of your project is to detect and classify various diseases affecting tomato leaves. This can help farmers monitor the health of their crops and take preventive measures. Tools and Technologies Used: Roboflow: You used Roboflow, a platform that helps create and manage datasets for computer vision tasks. It simplifies the process of uploading, labeling, and augmenting images, enabling efficient training of your classification model. Python: Python was the primary programming language for your project, used for building and training the model, data preprocessing, and managing the entire pipeline. Model Training: You trained an image classification model using the dataset of tomato leaf images, labeled with various disease categories. The model learns to classify leaf diseases based on visual patterns in the images. Image Classification: The heart of your project involves image classification, where the model assigns a label (disease type or healthy
Tomato Leaf Disease Classification
Project Overview: Objective: The goal of your project is to detect and classify various diseases affecting tomato leaves. This can help farmers monitor the health of their crops and take preventive measures. Tools and Technologies Used: Roboflow: You used Roboflow, a platform that helps create and manage datasets for computer vision tasks. It simplifies the process of uploading, labeling, and augmenting images, enabling efficient training of your classification model. Python: Python was the primary programming language for your project, used for building and training the model, data preprocessing, and managing the entire pipeline. Model Training: You trained an image classification model using the dataset of tomato leaf images, labeled with various disease categories. The model learns to classify leaf diseases based on visual patterns in the images. Image Classification: The heart of your project involves image classification, where the model assigns a label (disease type or healthy
Tomato Leaf Disease Classification
Project Overview: Objective: The goal of your project is to detect and classify various diseases affecting tomato leaves. This can help farmers monitor the health of their crops and take preventive measures. Tools and Technologies Used: Roboflow: You used Roboflow, a platform that helps create and manage datasets for computer vision tasks. It simplifies the process of uploading, labeling, and augmenting images, enabling efficient training of your classification model. Python: Python was the primary programming language for your project, used for building and training the model, data preprocessing, and managing the entire pipeline. Model Training: You trained an image classification model using the dataset of tomato leaf images, labeled with various disease categories. The model learns to classify leaf diseases based on visual patterns in the images. Image Classification: The heart of your project involves image classification, where the model assigns a label (disease type or healthy
Tomato Leaf Disease Classification
Tomato Leaf Disease Classification
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Experience
Data Science
Jul, 2024 - Dec, 2024
•
5 months
NUST
Jul, 2024 - Dec, 2024
•
5 months
In the time period of this course I complete projects on, data mining, machine learning, data visualization, and collaboration. The course emphasizes the use of popular tools like Python, SQL, Tableau, and Power BI.
Flag of
Gilgit, Pakistan
Jul, 2024 - Dec, 2024
•
5 months
Education
Computer Science
2022 - 2024
•
2 years
Flag of
Pakistan
2022 - 2024
•
2 years
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