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Scikit-learn VS bloop

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

Scikit-learn logo Scikit-learn

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

bloop logo bloop

Code-search engine for developers
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • bloop Landing page
    Landing page //
    2023-08-27

Scikit-learn videos

Learning Scikit-Learn (AI Adventures)

More videos:

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

bloop videos

Bloop - Review

More videos:

  • Tutorial - Bloop Korean Gel Nail Sticker Tutorial & Review | KBEAUTYHOBBIT
  • Review - BLOOP GEL IT WATER BASED NAIL POLISH PEELABLE PEEL OFF NAIL STICKERS NAIL GUARDS REVIEW

Category Popularity

0-100% (relative to Scikit-learn and bloop)
Data Science And Machine Learning
Productivity
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Developer Tools
0 0%
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 bloop

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...

bloop Reviews

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Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than bloop. It has been mentiond 29 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.

Scikit-learn mentions (29)

  • 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 / 22 days 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 / 4 months 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 1 year ago
  • WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point
    The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
  • PSA: You don't need fancy stuff to do good work.
    Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
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bloop mentions (9)

  • Reviewing AI Code Search Tools
    In this blog post, I’ll be comparing 3 distinct AI-first code search tools I recently came across: Cody (developed by late-stage startup, Sourcegraph), SeaGOAT (an open-source project that was trending on HN last week), and Bloop (an early-stage YC startup). I’ll be evaluating them along the dimensions of user-friendliness as well as their accuracy. - Source: dev.to / 9 months ago
  • Using Helium To Scrape Reedsy.com
    If you're confused about any of the code snippets above, you can check out bloop.ai and phind.com (along with its VSCode extension) to answer any of your questions about the repository, noting that both have free plans. - Source: dev.to / 10 months ago
  • Any GUI tools to explore objects?
    Bro let me turn your life inside out: https://bloop.ai. Source: about 1 year ago
  • With GPT-4, as a Software Engineer, this time I'm actually scared
    GPT4: Ok, here you go - https://bloop.ai/. Source: over 1 year ago
  • ChatGPT's API Is So Good and Cheap, It Makes Most Text Generating AI Obsolete
    We've invested a lot into helping LLMs reason and explain large codebases. We use a hybrid approach of local models for semantic search and a mix of OpenAI and Anthropic's models for language output and summarisation. We're two years in but everything still feels super early given how quickly the fundamentals are improving. Would love your feedback - https://bloop.ai. - Source: Hacker News / over 1 year ago
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What are some alternatives?

When comparing Scikit-learn and bloop, 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.

Productivity Power Tools - Extension for Visual Studio - A set of extensions to Visual Studio 2012 Professional (and above) which improves developer productivity.

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

Sourcegraph - Sourcegraph is a free, self-hosted code search and intelligence server that helps developers find, review, understand, and debug code. Use it with any Git code host for teams from 1 to 10,000+.

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

EssenceAI - Simplify Code Understanding using the power of GPT-4