Python Ecosystem – Part 2 – Python ML Ecosystem Libraries

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Python Ecosystem – Part 2 – Python ML Ecosystem Libraries

Python is an open-source ML language that is continually gaining popularity among the data science community. This is because it supports open-source tools available for machine learning and deep learning. However, there is no one size fits all tool for AI or ML subsets. Leading to the several core libraries serving a particular set of use cases or problems.


In this article, we’ll introduce you to some of the widely used Python libraries available for machine learning.


  1. 1. Scikit-learn –


Scikit-learn is one of the Python components which is widely used for the implementation of ML algorithms. It provides a clean uniform API platform that enables you to interact with a wide range of models.


Python Scikit-learn – Environment Setup


In order to install the Scikit-learn package, install the latest version of Python 3 using Anaconda or miniconda installers, and then run:


$ conda create -n sklearn-env -c conda-forge scikit-learn


$ conda activate sklearn-env


In order to check the installation, run the following command


$ conda list scikit-learn  # to see which scikit-learn version is installed


$ conda list  # to see all packages installed in the active conda environment


$ python -c “import sklearn; sklearn.show_versions()”


  1. 2. TensorFlow –


TensorFlow is a deep learning library that was originally developed by the Google Brain team. The library is built for numerical computation using data flow graphs which use the neural network of deep learning to help express and analyze patterns in large datasets.


Python TensorFlow – Environment Setup


conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0


python3 -m pip install tensorflow


# Verify install:


python3 -c “import tensorflow as tf; print(tf.config.list_physical_devices(‘GPU’))”


  1. 3. Keras –


TensorFlow, although a highly scalable and powerful deep learning library, is known for its user-friendly interface. Keras made TensorFlow simple by allowing for fast experimentation with the TensorFlow library.


Python Keras – Environment Setup


For Python Keras environment setup, run the following commands –


conda install python=3.6


conda create –name PythonGPU


activate PythonGPU


conda deactivate


conda install -c anaconda keras-gpu


conda install -c anaconda keras


conda install spyder


  1. 4. Pycaret


Python Pycaret – Environment Setup


conda create -n yourenvname python=x.x anaconda


conda activate yourenvname


conda deactivate


conda remove -n yourenvname –all


pip install pycaret


  1. 5. SciPy


SciPy is an ecosystem of Python libraries for mathematics, science, and engineering. It’s an add-on to Python needed for machine learning. It is comprised of three components – NumPy, Matplotlib, and Pandas.


Python SciPy – Environment Setup


For SciPy installation, the first step is to install Python. Once install confirm the installation was successful. Than open python interactive environment by typing “python” at the command line then run the following code –


# scipy


import scipy


print(‘scipy: %s’ % scipy.__version__)


# numpy


import numpy


print(‘numpy: %s’ % numpy.__version__)


# matplotlib


import matplotlib


print(‘matplotlib: %s’ % matplotlib.__version__)


# pandas


import pandas


print(‘pandas: %s’ % pandas.__version__)


In this article, we have covered the five most popular Python packages for machine learning. There are many other libraries available across the Python ecosystem including Theano, Chainer and Spark ML, etc that provide solutions for a specific set of problems.




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