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Ahrefs VS Scikit-learn

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

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Ahrefs logo 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!

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Ahrefs Landing page
    Landing page //
    2023-10-11

Ahrefs is trusted by SEOs and marketing professionals worldwide as the ultimate toolset for SEO, powered by industry-leading data. Ahrefs crawls the web, stores tons of data and makes it easily accessible via a simple user interface. The data can be used to aid keyword research, link building, content marketing and SEO strategies. Ultimately, the tool helps to accelerate the growth of organic search traffic to a website.

  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Ahrefs features and specs

  • Comprehensive Data
    Ahrefs offers extensive data on backlinks, keywords, and site audits, allowing users to make well-informed decisions on their SEO strategies.
  • User-Friendly Interface
    The platform has an intuitive and easy-to-navigate interface, making it accessible for both beginners and experienced SEO professionals.
  • Accurate Backlink Analysis
    Ahrefs is known for its accurate and up-to-date backlink data, which is crucial for comprehensive SEO analysis and strategy development.
  • Robust Keyword Research
    The keyword research tools in Ahrefs provide detailed information and insights, helping users to identify valuable keywords for their content.
  • Site Audit Capabilities
    Ahrefs' site audit feature helps identify and fix on-site SEO issues, improving overall website health and performance.
  • Continuous Updates
    Ahrefs frequently updates its database and introduces new features, ensuring users have access to the latest SEO tools and data.
  • Competitive Analysis
    The platform allows users to analyze competitor websites in-depth, giving insights into their strategies and helping to identify opportunities.

Possible disadvantages of Ahrefs

  • High Cost
    Ahrefs is relatively expensive compared to other SEO tools, which may be a barrier for small businesses or individual users with limited budgets.
  • Learning Curve
    Despite its user-friendly interface, the vast array of features and data can initially be overwhelming for new users, requiring time to master.
  • Limited Access in Basic Plan
    The lower-tier plans limit access to certain data and features, potentially necessitating an upgrade to higher-cost plans for full functionality.
  • No Free Trial
    Ahrefs does not offer a free trial, which can make it challenging for potential users to fully assess its value before committing to a subscription.
  • API Limitations
    Access to the API is restricted and may not be comprehensive enough for advanced users requiring extensive data integration capabilities.
  • Occasional Data Gaps
    Despite frequent updates, there may occasionally be gaps or delays in data, particularly for niche or emerging markets.
  • Limited Customer Support Options
    Customer support is mainly provided via email, which might not be sufficient for urgent issues or users preferring instant support options like live chat.

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 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.

Ahrefs videos

+ Add

Ahrefs Review and Tutorial: Is This The Only SEO Tool You Need?

More videos:

  • Review - Ahrefs Review | FatRank Ahref Testimonial
  • Tutorial - How to Use Ahrefs Tool - Best Premium SEO Tools [2019]

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Category Popularity

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

Ahrefs

Scikit-learn

SEO Tools
100 100%
SEO Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
SEO
100 100%
SEO
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Ahrefs and Scikit-learn. For example, how are they different and which one is better?
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Reviews

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

Ahrefs Reviews

  1. Cyra Brown
    · Owner at Beginu ·
    Excellent for discovering low competition keywords

    I've enjoyed using Ahrefs to inform content creation due to their keyword explorer being so useful for finding low difficulty keywords. I do prefer the legacy version of their site explorer in comparison to the new format so I hope that they do not retire certain elements of the platform.

    🏁 Competitors: SEMRush
The 16 Best Moz Alternatives for Every Budget 
Unlike competitors, Ahrefs doesn’t offer a free trial. To start using Ahrefs, you must purchase the Lite plan for $129.
Source: trafficthinktank.com
10 SE Ranking Alternatives in 2025 [Free and Paid]
Users appreciate Moz Pro for its user-friendly design and accurate rank tracking, making it accessible to both beginners and experienced marketers. However, some users feel it lacks the depth in backlink analysis offered by tools like Ahrefs, which may limit its appeal for those focusing on link-building.
Source: www.madx.digital
10 Moz Pro Alternatives in 2025 [Free and Paid]
Starting at $129/month, Ahrefs is slightly more expensive than Moz Pro but offers advanced features that justify the investment. For users who require detailed backlink data and in-depth SEO analysis, Ahrefs is a top choice.
Source: www.madx.digital
The best alternatives to SE Ranking in 2024
But all this comes at a price. Ahrefs is quite expensive, especially considering that even with a subscription, its use is not unlimited. At the beginning of each month, you are allocated a number of credits, which varies depending on the plan, and these credits are depleted as you use many of its features. And believe me, the credits run out faster than you realize.
Source: dinorank.com
Top 6 Moz Competitors In 2024: A Detailed Review
Furthermore, Ahrefs excels in providing users with in-depth backlink data. It helps to uncover new link-building opportunities and analyze competitors’ backlink strategies. Its user-friendly interface and accurate data make it a favorite among SEO professionals.
Source: www.flyingvgroup.com

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, Ahrefs should be more popular than Scikit-learn. It has been mentiond 119 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.

Ahrefs mentions (119)

  • Generating Content with ChatGPT
    I’ve been using the most excellent ARefs site to get information about how good the on-page SEO is for many of my sites. Every couple of weeks, ARefs crawls the site and will give me a list of suggestions of things I can improve. And for a long time, I had been putting off dealing with one of the biggest issues – because it seemed so difficult. - Source: dev.to / 7 days ago
  • How We Marketed a Niche SaaS Product with Zero Budget: 9 Strategies That Actually Worked
    Pro tip: Use Ahrefs or Ubersuggest to find long-tail gold. - Source: dev.to / 12 days ago
  • Ask HN: How to Get Good at SEO?
    I recently "launched" my product by mentioning it across Twitter and Discord which led some traffic to it. However, that is not a long-term strategy. I have heard about Ahrefs: https://ahrefs.com/, but I don't want to spend $129 right now since I'm not sure whether the ROI on it would be worth it. Are there any strategies or tips you might be able to share? - Source: Hacker News / about 1 month ago
  • Open source Google Analytics replacement
    Posthog is pretty good but very pushy towards using their SaaS (understandably). Self hosting is not really advertised on their main site however is buried in their gh repo as a footnote [1] with indications of vague issues past 100K events/month. Haven’t delved into how to scale it past that though and they do provide some docs that I have yet to review. Also the primary repo is not FOSS, and that "100% FOSS"... - Source: Hacker News / about 1 month ago
  • What We Did to Gain 3,000 GitHub Stars for the Liam Repository
    Used Ahrefs to check backlinks of competitors and similar products, adding sites that featured those products to our list of candidates. - Source: dev.to / about 2 months ago
View more

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 / almost 2 years ago
View more

What are some alternatives?

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

SEMRush - All-in-one Marketing Toolkit for digital marketing professionals.

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

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

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

Serpstat - Serpstat is the Swiss army knife for automating SEO processes. With a suite of powerful modules, you can track your performance, analyze your competitors, research keywords and backlinks, audit your website, and so much more.

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

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