Description:

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This dataset consists of a diverse collection of images, tailored specifically for the task of Animal Image Classification Dataset in the domain of animal species. It contains 15 distinct folders, each corresponding to a unique animal class, with each folder representing the name of the animal species. The dataset is composed of a variety of images that have been preprocessed and prepared for use in machine learning applications.

Dataset Details:

Image Size: Each image in the dataset has been resized to dimensions of 224x224 pixels with 3 color channels (RGB), making them suitable for immediate use in neural networks.

Data Source: Images were sourced from publicly available databases on the web. They encompass various environments, lighting conditions, and angles, ensuring a rich and diverse representation of each animal class.

Classes: The dataset includes 15 animal classes such as cats, dogs, birds, elephants, lions, and more, with each class represented by images stored in its respective folder.

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Preprocessing and Augmentation:

The dataset underwent extensive preprocessing using OpenCV libraries, ensuring that all images were standardized to the same size. In addition to resizing, multiple augmentation techniques were applied to diversify the dataset and improve model generalization. These augmentations include:

Rotation: Random rotations applied to simulate different perspectives.

Flipping: Horizontal flips to account for variations in animal orientation.

Cropping: Random cropping to focus on various parts of the animal subjects.

Scaling: Minor scaling adjustments to simulate different zoom levels.

All preprocessing and augmentation were carried out to enhance the robustness of any model trained on this data, without the need for further augmentation steps. Therefore, the dataset is ready for immediate use in training deep learning models such as CNNs (Convolutional Neural Networks) or transfer learning models.

Applications:

This dataset is ideal for:

Image Classification: Train models to accurately classify different animal species.

Transfer Learning: Utilize pre-trained models to fine-tune performance on this dataset.

Computer Vision Research: Explore various computer vision tasks, such as animal identification, object detection, and species recognition.

Wildlife and Conservation Studies: Use the dataset to build Al systems capable of identifying animals in the wild for tracking and conservation efforts.

Potential Use Cases:

Education: For students and researchers to learn and experiment with animal classification using computer vision techniques.

Al and Machine Learning Competitions: A challenging dataset for machine learning competitions centered around image classification.

Mobile Applications: Can be used to develop apps for real-time animal identification using image recognition technology.

Dataset Format:

The dataset is structured for ease of use, with each folder containing images pertaining to a specific class. The file format is as follows:

Folder Structure: dataset/{class_name}/{image_files.jpg}

Image Type: JPEG/PNG

Annotations: No specific annotations are included, but each folder name serves as the label for the images within it.

This dataset is sourced from Kaggle.

This Bangladeshi Brain Cancer MRI Dataset is a large dataset of Magnetic Resonance Imaging (MRI) images created to aid researchers in medical diagnosis, especially for brain cancer research. This collection contains a total of 1600 raw photos (every class have 400 raw images) after augmentation it contains total 6000 images, which are wisely divided into four main categories as:

Glioma -1500 images

Meningioma -1500 images

Pituitary-1500 images

No Tumor-1500 images

All the images in this dataset were collected from different hospitals around Bangladesh. It brought diversity and representation into the sample. To make the images compatible with various image processing, machine learning and deep-learning pipelines as possible they are then resized to a standardize size of 512×512.

This dataset is incredibly significant since high-quality data, such as medical imaging data, are few and difficult to obtain, particularly in the context of brain cancer. Assume that four prominent doctors collaborate on data collection in order to give more accurate and helpful content. It made it feasible. The cooperation emphasizes the dataset's potential to improve medical practice today by providing a dependable supply of diagnoses for use in diagnostic tool creation and testing within current medicine.

This dataset can be used by researchers and practitioners for a variety of applications such as Dense net 201, yolov8x/s, CNN, resnet50v2, VGG-16, MobilenetV2 etc.

Image Processing Details:

Images are randomly rotated within a range of 45 degrees. (rotation range=45)

Images are horizontally shifted by up to 20% of the width of the image. (width_shift_range=0.2)

Images are vertically shifted by up to 20% of the height of the image. (height_shift_range=0.2)

Shear transformation is applied to the image within a range of 20%. (shear range=0.2)

Images are randomly zoomed in or out by up to 20%. (zoom range=0.2)

Images are randomly flipped horizontally. (horizontal flip=True)

When transformations like rotations or shifts leave empty areas in the image, they are filled in by the nearest pixel values. (fill mode='nearest')

Hospital List(for Data Collection):

Ibn Sina Medical College, Kollanpur, 1, 1-B Mirpur Rd, Dhaka 1207

Dhaka Medical College & Hospital, Secretariat Rd, Dhaka 1000

Cumilla Medical College, Kuchaitoli, Dr. Akhtar Hameed Khan Road, Cumilla 3500, Bangladesh

Supervisor & investigator:

Md. Mizanur Rahman

Lecturer,

Computer Science and Engineering

Daffodil International University

Dhaka, Bangladesh

mizanurrahman.cse@diu.edu.bd

Data Collectors:

Md Shahriar Mannan Prottoy

Mahtab Chowdhury

Redwan Rahman

Azim Ullah Tamim

The dataset comprises images of three types of maize seeds: Wang Dataa, Sanzal Sima, and Bihilifa, sourced from Heritage Seeds Ghana. These maize varieties are commonly cultivated in the northern region of Ghana. At the collection point, Heritage Seeds Ghana manually sorted and labeled the images as either 'good' or 'bad' for each of the three varieties. The 'good' category represented high-quality maize seeds suitable for productive yields, while the 'bad' category included damaged, infected, or low-quality seeds not suitable for production. The images were captured using a 12-megapixel phone camera, resulting in original JPEG images of varying dimensions. Additionally, the augmented images were standardized to a size of 128 by 128. During capture, a blue background was used to ensure consistency and clarity during daylight, with no specific attention to lighting conditions. The images were then organized into their respective classes of 'good' and 'bad.' Overall, the dataset for this study comprises both raw (4,846 images) and augmented (28,910 images) color images.

Description:

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The Printed Digits Dataset is a comprehensive collection of approximately 3,000 grayscale images, specifically curate for numeric digit classification tasks. Originally create with 177 images, this dataset has undergone extensive augmentation to enhance its diversity and utility, making it an ideal resource for machine learning projects such as Sudoku digit recognition.

Dataset Composition:

Image Count: The dataset contains around 3,000 images, each representing a single numeric digit from 0 to 9.

Image Dimensions: Each image is standardized to a 28×28 pixel resolution, maintaining a consistent grayscale format.

Purpose: This dataset was develop with a specific focus on Sudoku digit classification. Notably, it includes blank images for the digit '0', reflecting the common occurrence of empty cells in Sudoku puzzles.

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Augmentation Details:

To expand the original dataset from 177 images to 3,000, a variety of data augmentation techniques were apply. These include:

Rotation: Images were rotated to simulate different orientations of printed digits.

Scaling: Variations in the size of digits were introduced to mimic real-world printing inconsistencies.

Translation: Digits were shifted within the image frame to represent slight misalignments often seen in printed text.

Noise Addition: Gaussian noise was added to simulate varying print quality and scanner imperfections.

Applications:

Sudoku Digit Recognition: Given its design, this dataset is particularly well-suited for training models to recognize and classify digits in Sudoku puzzles.

Handwritten Digit Classification: Although the dataset contains printed digits, it can be adapted and utilized in combination with handwritten digit datasets for broader numeric

classification tasks.

Optical Character Recognition (OCR): This dataset can also be valuable for training OCR systems, especially those aim at processing low-resolution or small-scale printed text.

Dataset Quality:

Uniformity: All images are uniformly scaled and aligned, providing a clean and consistent dataset for model training.

Diversity: Augmentation has significantly increased the diversity of digit representation, making the dataset robust for training deep learning models.

Usage Notes:

Zero Representation: Users should note that the digit '0' is represented by a blank image.

This design choice aligns with the specific application of Sudoku puzzle solving but may require adjustments if the dataset is use for other numeric classification tasks.

Preprocessing Required: While the dataset is ready for use, additional preprocessing steps, such as normalization or further augmentation, can be applied based on the specific requirements of the intended machine learning model.

File Format:

The images are stored in a standardized format compatible with most machine learning frameworks, ensuring ease of integration into existing workflows.

Conclusion: The Printed Digits Dataset offers a rich resource for those working on digit classification projects, particularly within the context of Sudoku or other numeric-based puzzles. Its extensive augmentation and attention to application-specific details make it a valuable asset for both academic research and practical Al development.

This dataset is sourced from Kaggle.

MODI script was used to write Indian languages as Marathi, Hindi, and Gujarati etc. from 12th century. From 17th century to mid of 19th century MODI was used as administrative script in Maharashtra state (India). Now a days, MODI script users are diminishing away, and countable persons can understand the MODI script. The archaic historical MODI handwritten documents contained important and rare cultural, historic, and administrative type of information which is usable in current era. In the research to train and test the Machine learning system a standard invariant character dataset is required. It is desirable in the development of the character recognition system that proposed approach has generalization proficiencies. The system gives good results if it is trained and tested using a standard invariant dataset. Here a standard invariant dataset of handwritten MODI characters is uploaded. MODI-HChar dataset contains total 57 handwritten MODI character classes images which comprises 10 numerals (0-9), 12 vowels (A – Ah) and 35 consonants (K - Dyn). This dataset includes total 575920 MODI character images as 101100 MODI digit images, 121320 MODI vowel images and 353500 MODI consonant images. This dataset is archived in a zip file. MODI-HChar dataset consists of three main folders as digits, vowels and consonants. Digits folder contains the subfolder for each digit zero to nine. Each of these folders includes 10110 images of the associated MODI digit. Equally vowel folder contains 12 subfolders and consonants folder contains 35 subfolders. And each of these subfolders contains 10110 images of the associated MODI character. The MODI character size is of 170x170 pixels and of 96 dpi. All the images are gray level and having type of the image is JPG.
The users of the MODI-HHDoc Dataset must agree that: • Use of the data set is restricted to research purpose only. • No redistribution of the dataset is allowed. • Dataset can be partitioned into training and testing as per the requirement. • In any resultant publications of research that uses the dataset, due credits will be provided to the following publication: - Deshmukh, M. S., Patil, M. P., & Kolhe, S. R. (2015, August). Off-line Handwritten Modi Numerals Recognition using Chain Code. In Proceedings of the Third International Symposium on Women in Computing and Informatics (pp. 388-393).

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Online Data Science Training Programs Market Size 2025-2029

The online data science training programs market size is forecast to increase by USD 8.67 billion, at a CAGR of 35.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for data science professionals in various industries. The job market offers lucrative opportunities for individuals with data science skills, making online training programs an attractive option for those seeking to upskill or reskill. Another key driver in the market is the adoption of microlearning and gamification techniques in data science training. These approaches make learning more engaging and accessible, allowing individuals to acquire new skills at their own pace. Furthermore, the availability of open-source learning materials has democratized access to data science education, enabling a larger pool of learners to enter the field. However, the market also faces challenges, including the need for continuous updates to keep up with the rapidly evolving data science landscape and the lack of standardization in online training programs, which can make it difficult for employers to assess the quality of graduates. Companies seeking to capitalize on market opportunities should focus on offering up-to-date, high-quality training programs that incorporate microlearning and gamification techniques, while also addressing the challenges of continuous updates and standardization. By doing so, they can differentiate themselves in a competitive market and meet the evolving needs of learners and employers alike.

What will be the Size of the Online Data Science Training Programs Market during the forecast period?

Request Free SampleThe online data science training market continues to evolve, driven by the increasing demand for data-driven insights and innovations across various sectors. Data science applications, from computer vision and deep learning to natural language processing and predictive analytics, are revolutionizing industries and transforming business operations. Industry case studies showcase the impact of data science in action, with big data and machine learning driving advancements in healthcare, finance, and retail. Virtual labs enable learners to gain hands-on experience, while data scientist salaries remain competitive and attractive. Cloud computing and data science platforms facilitate interactive learning and collaborative research, fostering a vibrant data science community. Data privacy and security concerns are addressed through advanced data governance and ethical frameworks. Data science libraries, such as TensorFlow and Scikit-Learn, streamline the development process, while data storytelling tools help communicate complex insights effectively. Data mining and predictive analytics enable organizations to uncover hidden trends and patterns, driving innovation and growth. The future of data science is bright, with ongoing research and development in areas like data ethics, data governance, and artificial intelligence. Data science conferences and education programs provide opportunities for professionals to expand their knowledge and expertise, ensuring they remain at the forefront of this dynamic field.

How is this Online Data Science Training Programs Industry segmented?

The online data science training programs industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeProfessional degree coursesCertification coursesApplicationStudentsWorking professionalsLanguageR programmingPythonBig MLSASOthersMethodLive streamingRecordedProgram TypeBootcampsCertificatesDegree ProgramsGeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Type Insights

The professional degree courses segment is estimated to witness significant growth during the forecast period.The market encompasses various segments catering to diverse learning needs. The professional degree course segment holds a significant position, offering comprehensive and in-depth training in data science. This segment's curriculum covers essential aspects such as statistical analysis, machine learning, data visualization, and data engineering. Delivered by industry professionals and academic experts, these courses ensure a high-quality education experience. Interactive learning environments, including live lectures, webinars, and group discussions, foster a collaborative and engaging experience. Data science applications, including deep learning, computer vision, and natural language processing, are integral to the market's growth. Data analysis, a crucial application, is gaining traction due to the increasing demand

description: Data presented here are subset of a larger plankton imagery data set collected in the subtropical Straits of Florida from 2014-05-28 to 2014-06-14. Imagery data were collected using the In Situ Ichthyoplankton Imaging System (ISIIS-2) as part of a NSF-funded project to assess the biophysical drivers affecting fine-scale interactions between larval fish, their prey, and predators. This subset of images was used in the inaugural National Data Science Bowl (www.datasciencebowl.com) hosted by Kaggle and sponsored by Booz Allen Hamilton. Data were originally collected to examine the biophysical drivers affecting fine scale (spatial) interactions between larval fish, their prey, and predators in a subtropical pelagic marine ecosystem. Image segments extracted from the raw data were sorted into 121 plankton classes, split 50:50 into train and test data sets, and provided for a machine learning competition (the National Data Science Bowl). There was no hierarchical relationships explicit in the 121 plankton classes, though the class naming convention and a tree-like diagram (see file "Plankton Relationships.pdf") indicated relationships between classes, whether it was taxonomic or structural (size and shape). We intend for this dataset to be available to the machine learning and computer vision community as a standard machine learning benchmark. This €œPlankton 1.0€ dataset is a medium-size dataset with a fair amount of complexity where image classification improvements can still be made.; abstract: Data presented here are subset of a larger plankton imagery data set collected in the subtropical Straits of Florida from 2014-05-28 to 2014-06-14. Imagery data were collected using the In Situ Ichthyoplankton Imaging System (ISIIS-2) as part of a NSF-funded project to assess the biophysical drivers affecting fine-scale interactions between larval fish, their prey, and predators. This subset of images was used in the inaugural National Data Science Bowl (www.datasciencebowl.com) hosted by Kaggle and sponsored by Booz Allen Hamilton. Data were originally collected to examine the biophysical drivers affecting fine scale (spatial) interactions between larval fish, their prey, and predators in a subtropical pelagic marine ecosystem. Image segments extracted from the raw data were sorted into 121 plankton classes, split 50:50 into train and test data sets, and provided for a machine learning competition (the National Data Science Bowl). There was no hierarchical relationships explicit in the 121 plankton classes, though the class naming convention and a tree-like diagram (see file "Plankton Relationships.pdf") indicated relationships between classes, whether it was taxonomic or structural (size and shape). We intend for this dataset to be available to the machine learning and computer vision community as a standard machine learning benchmark. This €œPlankton 1.0€ dataset is a medium-size dataset with a fair amount of complexity where image classification improvements can still be made.

Description:

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The Taiwan Tomato Leaves Dataset is an extensive and diverse collection tailored for research in plant pathology. With a particular focus on tomato leaf diseases. This dataset comprises 622 meticulously curated images, categorized into six distinct groups: five representing different tomato leaf diseases and one category denoting healthy leaves. These images provide a comprehensive resource for machine learning and computer vision applications. Especially in agricultural disease detection.

The dataset includes a variety of visual scenarios. Such as single leaf images, multiple leaf images, and leaves against both simple and complex backgrounds. The dataset's diversity in composition ensures a robust foundation for developing and testing disease detection models. Furthermore, the images in this dataset vary in their original dimensions but have been uniformly resized to 227 x 227 pixels for consistency. Which is ideal for use in CNNs (Convolutional Neural Networks) and other image-based machine learning models.

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Categories Covered:

Bacterial Spot: This category includes images of tomato leaves infected by the bacterium Xanthomonas campestris, which causes small, water-soaked lesions that can expand and result in tissue necrosis.

Black Leaf Mold: Featuring images of leaves affected by Pseudocercospora fuligena, a fungal disease that produces black spots and mold growth on the underside of leaves.

Gray Leaf Spot: This category captures symptoms of Stemphylium solani infection, characterized by grayish or brownish spots that can lead to leaf desiccation.

Healthy: This class contains images of undiseased tomato leaves, serving as the baseline for comparison against the diseased categories.

Late Blight: A fungal disease caused by Phytophthora infestans, late blight manifests as irregularly shaped lesions with water-soaked margins, often destroying the entire leaf.

Powdery Mildew: Powdery mildew, caused by Oidium neolycopersici, appears as white, powdery patches on the leaves, which can eventually result in chlorosis and leaf drop.

Dataset Features:

Image Diversity: The dataset is rich in visual variation, encompassing images of both singular and multiple leaves, as well as leaves presented against various backgrounds. This diversity helps to mimic real-world conditions where the appearance of leaves can be affected by environmental factors.

Standardized Image Size: To enhance usability in machine learning applications, all images have been resized to a uniform dimension of 227 x 227 pixels, ensuring compatibility with standard deep learning architectures.

Practical Use Cases: This dataset is highly suitable for training and evaluating models in the domains of plant disease classification, agricultural disease prediction, and automated plant health monitoring systems.

Potential Applications:

Agriculture: Supporting Al models that can identify and predict tomato plant diseases early, improving crop yield and reducing the need for manual inspection.

Education: Ideal for use in academic research and projects focused on machine learning, plant pathology, and Al-driven agricultural solutions.

Healthcare for Plants: Assisting farmers and agricultural experts in deploying automated disease detection tools to optimize plant health management.

This dataset is sourced from Kaggle.