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Code NASA VS Scikit-learn

Compare Code NASA VS Scikit-learn and see what are their differences

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Code NASA logo Code NASA

253 NASA open source software projects

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Code NASA Landing page
    Landing page //
    2023-10-15
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Code NASA features and specs

  • Open Access
    The platform provides open access to a wealth of software projects developed by NASA, making it easier for researchers, developers, and the public to utilize and contribute to advancements in technology and science.
  • Educational Value
    Offers educational opportunities by allowing students and educators to explore and use high-quality software from a leading scientific organization, fostering learning and innovation.
  • Collaborative Potential
    Encourages collaboration between NASA, educational institutions, private companies, and individual developers, which can lead to the enhancement and creation of new technologies.
  • Cost Savings
    Utilization of these open-source projects can lead to significant cost savings for organizations and developers by reducing the need to develop similar software from scratch.

Possible disadvantages of Code NASA

  • Limited Commercial Support
    The platform may not provide the level of commercial support that businesses might require, possibly complicating the integration of NASA's code into commercial products.
  • Complex Licensing
    Some projects may have complex licensing agreements that require careful review to ensure compliance, especially for commercial use.
  • Outdated or Discontinued Projects
    Some projects may be outdated or no longer actively maintained, which could pose challenges in terms of usability and security.
  • Technical Barrier
    There may be a high technical barrier to entry for some users, as the software is often highly specialized and may require expertise in particular domains to effectively implement.

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.

Code NASA 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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Scikit-learn

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Data Science Tools
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User comments

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Reviews

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

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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 Code NASA. 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.

Code NASA mentions (7)

  • NASA Stennis Releases First Open-Source Software
    Just to be clear this is one center’s first open source release. There’s open source from other centers at https://github.com/nasa. - Source: Hacker News / 5 days ago
  • FBI, Partners Dismantle Qakbot Infrastructure in Multinational Cyber Takedown
    NASA has a good set of open source projects available for public use: https://code.nasa.gov/. - Source: Hacker News / over 1 year ago
  • NASA's Software Catalog offers hundreds of new software programs for free
    Yes, this is no-cost but not necessarily open source. NASA open source software can be found at: https://code.nasa.gov/. - Source: Hacker News / almost 2 years ago
  • Public satellite telemetry data?
    As for public telemetry it might be hard to get it for free as satellite owners do it for money. NASA maintains a public software page at code.nasa.gov and software.nasa.gov which includes OpenMCT mission control software that can do simulated data. Source: over 3 years ago
  • Internship/research as a physics major
    Don't underestimate the strength of personal projects. If you ask a professor about their research, I find very often, they ask about things you have done in the past, which sort of feels like shit if youve done nothing huh? I know people who made cloud chambers or shot ions or massive simulations in HS and I was like, a theatre kid which is so irrelevant. BUT. The reason they ask this is that previous experience... Source: almost 4 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 / 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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What are some alternatives?

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

Google Open Source - All of Googles open source projects under a single umbrella

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

Open NASA - NASA data, tools, and resources

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

NASA Exoplanet Posters - Imagine visiting worlds outside our solar system

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

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