10 Selection Python Machine Learning Library

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Machine Learning

The branch of computer science in which such algorithms are designed that can learn is known as machine learning.
The experience learned from the tasks will improve overall learning to such an extent that it will become automatic and humans will not have to interfere or control the tasks.

Basic tasks in Machine learning are:

  • concept learning
  • function learning
  • predictive modeling
  • clustering
  • finding predictive patterns

Python Machine Learning

Neural networks and machine learning has been one of the hottest tech topics in tech in the past few years or so. Voice recognition, image recognition, music composition and a lot more make machine learning a fascinating subject. Here is a list of some of the best Python machine learning libraries.

1. TensorFlow

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TensorFlow is a high-level neural network library.
It helps you to program your network architectures while avoiding the low-level details. It allows you to express your computation as a data flow graph. It is written in C++. It includes Python bindings.
It has a flexible architecture.

2. Scikit-learn

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Scikit-learn library is one of the popular ML libraries out there.
It has several features for data mining and data analysis.
This makes it an excellent choice for researchers and developers.

3. Theano

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Theano is a machine learning library. Theano gives you the ability to define, then further optimize, and evaluate mathematical expressions that involve multi-dimensional arrays. This can be a point of frustration for some developers who use other libraries.

4. Pylearn2

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Pylearn2 has a pretty solid base.
Pylearn2 provides great flexibility.
It makes it possible for a researcher to do anything.

5. Pyevolve

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Pyevolveis a machine learning library that provides the developer with an amazing framework for building and executing genetic algorithm.

6. NuPIC

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NuPIC provides quite a different functionality than the normal and standard ML algorithms.
It is based on a theory of the neocortex which is called the Hierarchical Temporal Memory.

7. Pattern

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The pattern provides not only some ML algorithms, but it also provides you with tools to help you collect and analyze data.

8. Caffe

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Caffe is a library for machine learning in vision applications.
It is used to create deep neural networks that recognize a visual style.

9. Pandas

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Pandas is perfect for data wrangling.
Panda is designed for data manipulation and visualization.

10. Matplotlib

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Matplotlib is a python library that is used for powerful visualizations.

Conclusion

The Python libraries for machine learning have been discussed above.
According to your needs, you can choose the one that suits you.


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