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scikit-learn

Easy-to-use and general-purpose machine learning in Python
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What is scikit-learn?

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
scikit-learn is a tool in the Machine Learning Tools category of a tech stack.
scikit-learn is an open source tool with 52.7K GitHub stars and 23.9K GitHub forks. Here’s a link to scikit-learn's open source repository on GitHub

Who uses scikit-learn?

Companies
200 companies reportedly use scikit-learn in their tech stacks, including Delivery Hero, Tokopedia, and RatePAY GmbH.

Developers
802 developers on StackShare have stated that they use scikit-learn.

scikit-learn Integrations

Jupyter, Keras, Ludwig, Comet.ml, and cnvrg.io are some of the popular tools that integrate with scikit-learn. Here's a list of all 22 tools that integrate with scikit-learn.
Pros of scikit-learn
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Scientific computing
18
Easy
Decisions about scikit-learn

Here are some stack decisions, common use cases and reviews by companies and developers who chose scikit-learn in their tech stack.

Should I continue learning Django or take this Spring opportunity? I have been coding in python for about 2 years. I am currently learning Django and I am enjoying it. I also have some knowledge of data science libraries (Pandas, NumPy, scikit-learn, PyTorch). I am currently enhancing my web development and software engineering skills and may shift later into data science since I came from a medical background. The issue is that I am offered now a very trustworthy 9 months program teaching Java/Spring. The graduates of this program work directly in well know tech companies. Although I have been planning to continue with my Python, the other opportunity makes me hesitant since it will put me to work in a specific roadmap with deadlines and mentors. I also found on glassdoor that Spring jobs are way more than Django. Should I apply for this program or continue my journey?

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Blog Posts

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scikit-learn Alternatives & Comparisons

What are some alternatives to scikit-learn?
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
H2O
H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
XGBoost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
See all alternatives

scikit-learn's Followers
1054 developers follow scikit-learn to keep up with related blogs and decisions.