Top 12 Python Libraries for Machine Learning Tasks in 2020

Top Python Libraries for Machine Learning

Numpy

  • Selection and sorting capabilities.
  • Shape manipulation.
  • Random simulations.
  • Statistical operations.
  • Logical operations.

TensorFlow

  • Natural language processing.
  • Deep neural networks.
  • Partial differential equations (PDEs).
  • Text, speech, and image recognition.
  • Abstract capabilities.
  • Effortless collaboration of ideas and code.

Keras

PyTorch

  • It’s easy to use, learn, and integrate with the entire Python ecosystem.
  • Tensor computing and the ability to support accelerated processing using Graphics Processing units (GPUs).
  • Supports neural networks built on auto diff systems (tape-based).

Scikit-learn

Pandas

  • Dataset pivoting and reshaping.
  • Joining and merging datasets.
  • Data alignment and handling missing data.
  • Indexing options like Fancy indexing and Hierarchical axis indexing.
  • Data filtration.

NLTK

  • Searching for keywords from documents.
  • Classification and tokenization of texts.
  • Handwriting and voice recognition.
  • Word Lemmatization and stemming.

Spark MLlib

  • Regression
  • Optimization
  • Clustering
  • Dimensional Reduction
  • Basic Statistics
  • Classification
  • Feature Extraction

Theano

  • GPU support that makes it suitable for performing heavy computations.
  • Supports integration with NumPy.
  • Stable and faster evaluations of variables, including the complex ones.
  • Supports creation of C code to perform mathematical operations.

MXNet

Matplotlib

SciPy

Conclusion

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