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Scikit-learn VS Ximilar

Compare Scikit-learn VS Ximilar and see what are their differences

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Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Ximilar logo Ximilar

Ximilar is a Computer Vision platform that allows you to build and train Deep Learning models for Image Recognition, Detection, and Visual Search. Allows you to download a model for offline usage or connect to them via API.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Ximilar Landing page
    Landing page //
    2023-06-25

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

Ximilar features and specs

  • Ease of Use
    Ximilar provides a user-friendly interface and intuitive tools, making it accessible for developers with varying levels of expertise.
  • Custom Model Training
    Allows users to train their own models on personalized datasets, which can be tailored to specific business needs and unique applications.
  • Pre-built Models
    Offers a variety of pre-built models that can be used out-of-the-box, saving time for businesses needing quick deployment of certain image recognition tasks.
  • API Access
    Provides robust API access which facilitates integration with existing systems and workflows, enhancing the versatility of its solutions.
  • Scalability
    Can handle large data volumes and scale with business growth, making it suitable for enterprises of various sizes.

Possible disadvantages of Ximilar

  • Cost
    Possible high costs associated with extensive use or specialized features, which may not be feasible for smaller businesses or projects with limited budgets.
  • Limited Niche Applications
    While it offers general pre-built models, some niche applications may require more customization than what is provided out-of-the-box.
  • Dependence on Internet Connectivity
    Relies on cloud services for data processing, which can be a downside in areas with poor internet connectivity or for applications needing offline capabilities.
  • Learning Curve for Custom Features
    While the platform is generally easy to use, more advanced or custom features may present a learning curve for users unfamiliar with machine learning concepts.
  • Data Privacy Concerns
    Utilizing cloud-based solutions may raise concerns regarding data privacy and security, particularly for industries dealing with sensitive information.

Scikit-learn videos

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Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Ximilar videos

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Category Popularity

0-100% (relative to Scikit-learn and Ximilar)

Scikit-learn

Ximilar

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Image Analysis
0 0%
Image Analysis
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Machine Learning
0 0%
Machine Learning
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Scikit-learn and Ximilar

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...
Source: searchbusinessanalytics.techtarget.com

Ximilar Reviews

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Social recommendations and mentions

Based on our record, Scikit-learn seems to be a lot more popular than Ximilar. While we know about 31 links to Scikit-learn, we've tracked only 1 mention of Ximilar. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 11 months ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / about 1 year ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
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Ximilar mentions (1)

  • Launched a sports card search engine...seeking your feedback
    Looks great. It would be great if it would be possible to search by image/photo from smartphone, you could build a mobile app arount it or integrate in on website. We at ximilar.com can train your customized image AI model with API that is tuned for sports cards. Just contact us at [info@ximilar.com](mailto:info@ximilar.com). Source: over 3 years ago

What are some alternatives?

When comparing Scikit-learn and Ximilar, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

VISUA - We are the Visual-AI people. Providing industry-leading enterprise computer vision technologies, including Image Recognition, Object & Scene Detection and more. We believe Visual-AI liberates people and brands to do, create and discover more.

OpenCV - OpenCV is the world's biggest computer vision library

CompreFace - CompreFace is a free face recognition service from Exadel that can be easily integrated into any system using simple REST API.

NumPy - NumPy is the fundamental package for scientific computing with Python

Google Vision AI - Cloud Vision API provides a comprehensive set of capabilities including object detection, ocr, explicit content, face, logo, and landmark detection.

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