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Scikit-learn VS Keywords Everywhere

Compare Scikit-learn VS Keywords Everywhere 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.

Keywords Everywhere logo Keywords Everywhere

Free browser add-on for keyword volume, CPC & competition
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Keywords Everywhere Landing page
    Landing page //
    2023-09-19

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.

Keywords Everywhere features and specs

  • Comprehensive Metrics
    Keywords Everywhere provides detailed metrics such as search volume, CPC, and competition data, helping users make informed decisions for SEO and PPC strategies.
  • Ease of Use
    The browser extension integrates seamlessly with essential tools like Google Search, YouTube, and Google Analytics, making it convenient to access keyword data directly from these platforms.
  • Affordability
    Offers a pay-as-you-go pricing model, which can be more cost-effective for small businesses and individual users compared to subscription-based services.
  • Data Across Platforms
    Provides keyword data for multiple platforms including Google, YouTube, Amazon, and more, which is valuable for diverse digital marketing strategies.
  • Time Saver
    By displaying keyword metrics directly in search engine results and other tools, it significantly reduces the time needed to gather and analyze keyword data.

Possible disadvantages of Keywords Everywhere

  • Limited Free Version
    The free version offers very limited features, driving users to purchase credits for more comprehensive data.
  • Dependency on Browser Extension
    Requires a browser extension to function, which may not be suitable for all users or devices and could raise privacy/security concerns.
  • Accuracy Variability
    As with many keyword tools, the accuracy of the data can occasionally be inconsistent, which may affect strategic decisions.
  • Limited Advanced Features
    While great for basic keyword research, it lacks some of the advanced features offered by more robust SEO tools, such as detailed competitive analysis or site audits.
  • Potential for Data Overload
    The abundance of data displayed can sometimes be overwhelming, particularly for beginners who may struggle to interpret and utilize it effectively.

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.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Keywords Everywhere videos

+ Add

How to use Keywords Everywhere - SEO keyword research tool

More videos:

  • Review - KEYWORDS EVERYWHERE is now a PAID TOOL - Here's What To Do - Keywords Everywhere Alternative
  • Tutorial - Keywords Everywhere | A Tutorial + Advice on Keywords for YouTube
  • Review - Keywords Everywhere Review: Better Alternative to Google Keyword Planner
  • Review - Keywords Everywhere Review | Best Keyword Search Volume Chrome Extension! 🚀
  • Review - Keywords Everywhere Review 2021 | Keyword research Tool

Category Popularity

0-100% (relative to Scikit-learn and Keywords Everywhere)

Scikit-learn

Keywords Everywhere

Data Science And Machine Learning
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Data Science And Machine Learning
0% 0
SEO Tools
0 0%
SEO Tools
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
SEO
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SEO
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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 Scikit-learn and Keywords Everywhere

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

Keywords Everywhere Reviews

112 Best Chrome Extensions You Should Try (2021 List)
Keywords Everywhere is an alternative to Ubersuggest, a freemium keyword research tool. It shows the search query data on more than 15 websites. For free users, it shows a trend chart, long-tail keywords, and keywords from ‘people also search for’. But, paid users can see monthly search volume, CPC, competition, and trend data. Although solely for keyword research, you do...
Source: www.einsstark.tech
9 Free Keyword Research Tools (That CRUSH Google Keyword Planner)
Keywords Everywhere is a free add-on for Chrome (or Firefox). It adds search volume, CPC & competition data to all your favourite websites.
Source: ahrefs.com
73 Best SEO tools 2021 – The Most Epic List You Shouldn’t Miss
While most use this tool strictly for Paid ads, Keywords Everywhere is very useful to help you discover long-tail keywords related to the ones you are searching for on Google.
Source: blog.linkody.com

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Keywords Everywhere. 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.

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 / 12 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 / 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 / almost 2 years ago
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Keywords Everywhere mentions (16)

  • SEO 101 for Software Developers
    To find keywords I use the tool Keywords Everywhere. It gives you information on how many people search for a particular keyword a month, how difficult it will be to rank for, as well ideas for additional keywords. - Source: dev.to / over 1 year ago
  • How to Manage Your Time as a Software Developer ⌛️
    For example, I do a lot of keyword research for my blog posts and YouTube videos. This generally consists of searching for keywords on Google and then copying the numbers that I get from Keywords Everywhere into a spreadsheet. - Source: dev.to / about 2 years ago
  • My Guide To Shopify Store Keyword Research
    You may be thinking to yourself well that's it right? I know what works and what doesn't, well not exactly because you don't just want to copy everything your competition does or you'll be competing with them all the time and that's a losing battle for most small stores. So step 2 is I cross reference it with another tool called keywords everywhere. As I mentioned this tool can be similar to Ahrefs as you can scan... Source: about 2 years ago
  • GMB Stats?
    Keywords everywhere again, not sure if it's match for you. Source: about 2 years ago
  • Keyword research
    Step 2: keywordseverywhere.com ($10 for 100K SV check - it's a chrome extension), run your list through this and get all SV. Source: about 2 years ago
View more

What are some alternatives?

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

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

KeywordTool.io - KeywordTool.io is the best FREE alternative to Google Keyword Planner and Ubersuggest. It uses Google's autocomplete feature to get over 750+ long-tail keywords for any given query.

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

Moz - Backed by industry-leading data and the largest community of SEOs on the planet, Moz builds tools that make inbound marketing easy.

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

Ahrefs - Ahrefs is a toolset for SEO and marketing. We have tools for backlink research, organic traffic research, keyword research, content marketing & more. Give Ahrefs a try!

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