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API List VS Scikit-learn

Compare API List VS Scikit-learn and see what are their differences

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API List logo API List

A collective list of APIs. Build something.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • API List Landing page
    Landing page //
    2021-09-21
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

API List features and specs

  • Variety
    API List provides a diverse range of APIs in various categories such as entertainment, data, weather, and more, making it easy to find APIs that suit different needs.
  • Ease of Access
    The platform is user-friendly and allows users to quickly browse and discover APIs without complex navigation or extensive searches.
  • Free APIs
    Many of the APIs listed on the site are free to use, which is a great advantage for developers who are looking for cost-effective solutions.
  • Updated Content
    The list appears to be maintained and updated regularly, ensuring that users have access to current and functional APIs.

Possible disadvantages of API List

  • Quality Variation
    The quality and reliability of the listed APIs can vary significantly since they come from different sources and may not all be thoroughly vetted.
  • Limited Information
    Some API listings may lack detailed descriptions or documentation links, which can make it harder for developers to assess their suitability.
  • No User Reviews
    The site does not provide a mechanism for user feedback or reviews, which could help other users to determine the usefulness and reliability of an API.
  • Possible Downtime
    There is no guarantee of uptime for the APIs listed, and some may experience downtimes or discontinuation without prior notice.

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.

Analysis of API List

Overall verdict

  • API List is a useful resource for developers seeking to explore various APIs across different categories. It simplifies the process of discovering APIs and provides quick access to essential information. However, like any curated directory, the quality and completeness of information about each API may vary.

Why this product is good

  • API List (apilist.fun) is a curated directory of APIs that can be helpful for developers looking for new APIs to integrate into their applications. It organizes APIs into categories, making it easier to discover tools that fit specific needs. The site often provides basic information about each API, along with links to their documentation, which can save time for developers in the exploration phase.

Recommended for

    API List is recommended for developers, software engineers, and project managers who are seeking new APIs to integrate, particularly those who are in the early stages of project planning and need an efficient way to explore available options.

Analysis of Scikit-learn

Overall verdict

  • Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.

Why this product is good

  • Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.

Recommended for

  • Beginners learning machine learning concepts and application.
  • Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
  • Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
  • Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.

API List videos

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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.

Category Popularity

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API List

Scikit-learn

APIs
100 100%
APIs
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Data Science And Machine Learning
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Data Science And Machine Learning
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
Data Science Tools
0 0%
Data Science Tools
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 API List and Scikit-learn

API List Reviews

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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

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than API List. It has been mentiond 31 times since March 2021. 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.

API List mentions (18)

  • How to Promote and Market your API: API Directories
    This simple and intuitive website categorizes APIs (and allows for multiple categories per API). Some social aspects are introduced; like upvotes, comments, list of companies using the API. - Source: dev.to / 6 months ago
  • Promises in JavaScript: Understanding, Handling, and Mastering Async Code
    If you haven’t tried it yet, I recommend writing a simple code snippet to fetch data from an API. You can start with a fun API to experiment with. Plus, all the examples and code snippets are available in this repository for you to explore. - Source: dev.to / 10 months ago
  • What’s the most exciting API you discovered?
    I don't know any good ones specifically, but https://apilist.fun was helpful back when I was playing around. Source: about 2 years ago
  • Boost Your Next Project with My Comprehensive List of Free APIs – 1000+ and Counting!
    Public-api Github Repo : https://github.com/public-apis/public-apis Rapid API : https://rapidapi.com/collection/list-... API House : https://apihouse.vercel.app/ Free APIs: https://free-apis.github.io/#/ Dev Resources : https://devresourc.es/tools-and-utili... AnyApi: https://any-api.com/ Public Apis : https://public-apis.io/ API List : https://apilist.fun/ Public APIs: https://public-apis.xyz/ Public... - Source: dev.to / about 2 years ago
  • A Beginner Developer's Guide to APIs (with Example Project)
    There are hundreds of APIs available for you to use in your projects. API List is a comprehensive list of publicly available APIs and links to the documentation and other important information for each API. - Source: dev.to / over 2 years ago
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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 / 4 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 / 6 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 / about 1 year 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 / over 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 / about 2 years ago
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What are some alternatives?

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

Abstract APIs - Simple, powerful APIs for everyday dev tasks

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

PublicAPIs - Explore the largest API directory in the galaxy

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

JSONREPO - JSONREPO is an API platform created for developers seeking fast, reliable, and scalable APIs

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

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