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MLJAR VS Scikit-learn

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

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MLJAR logo MLJAR

MLJAR is a predictive analytics platform that facilitates machine learning algorithms search and tuning.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • MLJAR Landing page
    Landing page //
    2023-06-14
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

MLJAR 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

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

MLJAR

Scikit-learn

Data Science And Machine Learning
4 4%
Data Science And Machine Learning
96% 96
Data Science Tools
3 3%
Data Science Tools
97% 97
AI
100 100%
AI
0% 0
Python Tools
0 0%
Python 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 MLJAR and Scikit-learn

MLJAR 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 MLJAR. It has been mentiond 27 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.

MLJAR mentions (3)

  • [P] Build data web apps in Jupyter Notebook with Python only
    Sure, at the bottom of our website you can subscribe for newsletter. Source: about 1 year ago
  • Data Science and full-stack-web development
    In my case, I had experience in DS and software engineering. It gives me ability to start a company that works on Data Science tools. Source: about 2 years ago
  • [D] Bring your own data AI SaaS service for non-programmers?
    Instead, we started to work on desktop application that will allow to create python notebooks with no-code GUI (https://github.com/mljar/studio some screenshots on our website ). Source: over 2 years ago

Scikit-learn mentions (27)

  • 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 / 11 months 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: 12 months 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: 12 months ago
  • Help on using R for Machine Learning?
    Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: about 1 year ago
  • Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
    This is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning... - Source: dev.to / about 1 year ago
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What are some alternatives?

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

Google Cloud Machine Learning - Google Cloud Machine Learning is a service that enables user to easily build machine learning models, that work on any type of data, of any size.

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

Amazon Machine Learning - Machine learning made easy for developers of any skill level

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

Teachable Machine - Easily create machine learning models for your apps, no coding required.

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

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