Top machine learning libraries for Python
1. Numpy
Numerical Python
It is the most fundamental package for scientific computing in python. It provides operations for matrix and array. Numpy arrays are used in most of the ML projects. The library provides vectorization of mathematical operations on the NumPy array type
2. Scipy
modules for linear algebra, optimization, integration, and statistics. It contains modules for linear algebra, optimization, integration, and statistics.
3. Pandas
It works with labelled and relational data.
It designed for quick and easy data manipulation, aggregation, and visualization.
Here is just a small list of things that you can do with Pandas:
Easily delete and add columns from DataFrame
Convert data structures to DataFrame objects
Handle missing data, represents as NaNs
Powerful grouping by functionality
4. Matplotlib
Used for generation of simple and powerful visualizations .
With a bit of effort you can make just about any visualizations:
Line plots;
Scatter plots;
Bar charts and Histograms;
Pie charts;
Stem plots;
Contour plots;
Quiver plots;
Spectrograms.
5. Seaborn
for visualisation of statistical models
6. Bokeh
Used for interactive visualizations.
7. Plotly
Web based tool for visualizations
8. SciKit-Learn
The scikit-learn exposes a concise and consistent interface to the common machine learning algorithms, making it simple to bring ML into production systems.
9. Theano
* It defines multi-dimensional arrays
* And math operations and expressions
10. TensorFlow
11. Keras
12. NLTK
13.Gensim
14. Scrapy
15. Statsmodels
refer for more : https://medium.com/activewizards-machine-learning-company/top-15-python-libraries-for-data-science-in-in-2017-ab61b4f9b4a7
1. Numpy
Numerical Python
It is the most fundamental package for scientific computing in python. It provides operations for matrix and array. Numpy arrays are used in most of the ML projects. The library provides vectorization of mathematical operations on the NumPy array type
2. Scipy
modules for linear algebra, optimization, integration, and statistics. It contains modules for linear algebra, optimization, integration, and statistics.
3. Pandas
It works with labelled and relational data.
It designed for quick and easy data manipulation, aggregation, and visualization.
Here is just a small list of things that you can do with Pandas:
Easily delete and add columns from DataFrame
Convert data structures to DataFrame objects
Handle missing data, represents as NaNs
Powerful grouping by functionality
4. Matplotlib
Used for generation of simple and powerful visualizations .
With a bit of effort you can make just about any visualizations:
Line plots;
Scatter plots;
Bar charts and Histograms;
Pie charts;
Stem plots;
Contour plots;
Quiver plots;
Spectrograms.
5. Seaborn
for visualisation of statistical models
6. Bokeh
Used for interactive visualizations.
7. Plotly
Web based tool for visualizations
8. SciKit-Learn
The scikit-learn exposes a concise and consistent interface to the common machine learning algorithms, making it simple to bring ML into production systems.
9. Theano
* It defines multi-dimensional arrays
* And math operations and expressions
10. TensorFlow
11. Keras
12. NLTK
13.Gensim
14. Scrapy
15. Statsmodels
refer for more : https://medium.com/activewizards-machine-learning-company/top-15-python-libraries-for-data-science-in-in-2017-ab61b4f9b4a7
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