Top 5 Libraries of Machine Learning in Python

Install them on Ubuntu 20.04 LTS

The field of data science relies heavily on the predictive capability of Machine Learning (ML) algorithms. Python offers an opportune playground for experimenting with these algorithms due to the readability and syntactical efficiency of the language. The vast availability of ML libraries accessible to Python users makes it an even more attractive solution to interpret the immense amount of data available today.

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Python libraries are specific files containing pre-written code that can be imported into your code base by using Python’s import feature. This increases your code reusability.

1. NumPy
2. SciPy
3. Scikit-learn
4. Matplotlib
5. TensorFlow 

Install these libraries in ubuntu 20.04 LTS via python package installer (pip).

First, install the latest version of pip in your ubuntu machine then install all five libraries using this tool.

Install Python and pip

Before installing any software, first update the APT repository. Then install software as per your requirements.

Once everything is updated now you can install python3 in your machine.

# sudo apt update && sudo apt install python3 && sudo apt install python3-pip

To verify that pip and python3 was successfully installed, run the following command:

# python3 --version # pip3 --

version

You should see output similar to this:

We have successfully installed python and pip. Now we could install the package for Python Virtual Environment.

Numpy

NumPy adds multi-dimensional array and matrix processing to Python, as well as a large collection of high-level mathematical functions. It is commonly used for scientific computing and hence, one of the most used Python Packages for machine learning.

 

Now we can install packages using the pip3 command.

# pip3 install numpy

Once it is installed, run a version check to verify that the installation has completed successfully:

# pip3 freeze | grep numpy

Now, we can import libraries for our use and use per our requirement.

• SciPy

Scipy is a very popular Machine Learning library with huge features. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing,

Now we can install packages using the pip3 command.

# pip3 install scipy

# pip3 freeze | grep scipy

• Scikit-learn

In 2007, David Cournapeau developed the Scikit-learn library as part of the Google Summer of Code project. In 2010 INRIA became involved and did the public release in January 2010.

Scikit-learn was built on top of two Python libraries – NumPy and SciPy and has become the most popular Python machine learning library for developing machine learning algorithms.

Scikit-learn has a wide range of supervised and unsupervised learning algorithms that works

on a consistent interface in Python.The Python library,Scikit-Learn, is built on top of the matplotlib, NumPy, and SciPy libraries. This Python ML library has several tools for data analysis and data mining tasks.

# pip3 install scikit-learn

# python3 -m pip show scikit-learn

• Matplotlib

Matplotlib is a data visualization library that is used for 2D plotting to produce publication-quality image plots and figures in a variety of formats. The library helps to generate histograms, plots, error charts, scatter plots, bar charts with just a few lines of code.

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Flexible usage: supports both Python and IPython shells, Python scripts, Jupyter Notebook, web application servers and many GUI toolkits (GTK+, Tkinter, Qt, and wxPython).

# pip install matplotlib

# python3 -m pip show matplotlib

TensorFlow

TensorFlow was developed for Google’s internal use by the Google Brain team. Its first release came in November 2015 under Apache License 2.0. TensorFlow is a popular computational framework for creating machine learning models. TensorFlow supports a variety of different toolkits for constructing models at varying levels of abstraction.

TensorFlow exposes a very stable Python and C++ APIs. It can expose backward compatible APIs for other languages too, but they might be unstable. TensorFlow has a flexible architecture with which it can run on a variety of computational platforms CPUs, GPUs, and TPUs. TPU stands for Tensor processing unit, a hardware chip built around TensorFlow for machine learning and artificial intelligence.

# pip3 install

Wrap Up:

You should be prepared to dive in, explore, and experiment with one of the most interesting libraries of the future of programming: machine learning. To get started, you can:

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Thank You For Reading

Written by Sachin Saini