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NLTK

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NLTK vs scikit-learn: What are the differences?

NLTK: It is a leading platform for building Python programs to work with human language data. It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language; scikit-learn: Easy-to-use and general-purpose machine learning in Python. scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

NLTK and scikit-learn can be categorized as "Machine Learning" tools.

scikit-learn is an open source tool with 36.5K GitHub stars and 17.9K GitHub forks. Here's a link to scikit-learn's open source repository on GitHub.

According to the StackShare community, scikit-learn has a broader approval, being mentioned in 104 company stacks & 252 developers stacks; compared to NLTK, which is listed in 15 company stacks and 17 developer stacks.

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    Cons of NLTK
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      - No public GitHub repository available -

      What is NLTK?

      It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

      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.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use NLTK?
      What companies use scikit-learn?
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      What tools integrate with NLTK?
      What tools integrate with scikit-learn?

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      What are some alternatives to NLTK and scikit-learn?
      SpaCy
      It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
      Gensim
      It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
      TensorFlow
      TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
      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/
      See all alternatives