Machine Learning libraries || Top 4 Machine Learning libraries

Every machine-learning work project has strong machine libraries that support it to ensure rapid and successful developments of the project. As an AI engineer, the libraries that I am going to discuss in this article are highly valuable. These libraries will assist you in your AI task whenever you are working in AI.

 Table of Contact:

Pandas

Pandas

Pandas is a Python library that performs analysis and manipulation of data. Structured data can be easily handled and manipulated by providing data structures such as DataFrame and Series.


Key Features

  1. It is a matrix that accommodates different data types in one dimension.
  2. Pandas is good with missing values, duplicates, and outliers.
  3. Fast and simple aggregation of data sets.
  4. Suitable for use on timestamped data.

How to install:

import pandas

This command installs the Pandas library. 

You can you pandas functions and classes with the prefix ‘pandas’. Such as

 pandas.DataFrame or pandas.read_csv().

OR

import pandas as pd

It makes it more convenient to reference the functions and classes of Pandas. For example, you can type pd.read_csv() instead of pandas.read_csv().

Windows: 

If you using any IDE like anaconda or python etc:

pip install pandas

OR

pip3 install pandas


Ubuntu/ linux:

sudo pip install pandas

OR

sudo pip3 install pandas

Matplotlib

Matplotlib
It is one of the most powerful 2D plotting libraries that can be used in an interactive notebook to produce high quality, static, or animated visualizations in Python. The software of this is a  preferred tool by  researchers, data scientists for data exploration and presentations.


Key Features

  1. It provides : line plots, pie charts, bar plots, line charts,  etc.
  2. In sciences it produces charts that are ideal for publication.
  3. Seamlessly integrates with Jupyter Notebooks.

 How to import

import matplotlib.pyplot

This command installs the pyplot module from the matplotlib. You can use matplotlib with the prefix of 'matplotlib.pyplot'. 

For example:

matplotlib.pyplot.plot(x, y)

matplotlib.pyplot.show()

 OR

import matpltlib.pyplot as plt

This alias makes it more convenient to reference pyplot module. For example, you can use plt.pyplot.plot(x, y)  instead of typing out matplotlib.pyplot.plot(x, y)

Windows: 

If you using any IDE like anaconda or python etc:

pip install matplotlib

OR

pip3 install matplotlib


Ubuntu/ linux:

sudo pip install matplotlib

OR

sudo pip3 install matplotlib


NumPy
numPy

It is a basic module for scientific computation in the python framework. It supports huge multidimensional arrays and matrices as well as a suite of operators to work over these structures.

Key Features

  1.  Efficient manipulation of large datasets.
  2. Combine arrays of any size and shape. 
  3.  The suite of functions, which can be done by linear algebra algebra operations.
  4.  It is vital in simulations and statistics.

How to import:

import numpy

import numpy

This statemnet imports the numpy library, and you can use all the classes and functions of numpy with the keyword ‘numpy’

z = numpy.array([1,2,3,])

OR

import numpy as np

import numpy as np

This command imports the numpy library, and you can use the functions and classes with the prefix ‘np’.

Z = np.array([1,2,3,])

It makes it more convenient. For example, you can use np.array([1,2,3,])

Windows: 

If you using any IDE like anaconda or python etc:

pip install numpy

OR

pip3 install numpy


Ubuntu/ linux:

pip install numpy

OR

pip3 install numpy

instead of typing out numpy.array([1,2,3,])

SciPy

SciPy
SciPy is built in a way that it leverages NumPy’s power in support of scientific computation. There are optimization, integration, interpolation, signal and image as well as statistical functions components in it.

Key Features

  1. Robust tools for optimization problems.
  2. Numerical methods of approximation for integral limits and improper limits.
  3. These include filters, spectral analysis, and signal transformations.
  4. Statistical computational environments include extensive statistical routines for hypothesis testing, probability distributions, and so forth.

How to import:

import scipy

This command import whole SciPy library and you can access its various submodules and functions.

For specific submodules or functions from SciPy:

from scipy import clusture


Windows: 

If you using any IDE like anaconda or python etc:

pip install scipy

OR

pip3 install scipy


Ubuntu/ debian:

sudo apt-get install python-scipy

OR

Fedora:

sudo dnf install python-scipy


Conclusion

The art of handling data in Machine learning includes pandas, matplotlib, numpy, and scipy – an unavoidable ability.  These libraries together comprise a toolkit of strong tools that allow to deal with data visualization, manipulation, and scientific computation. A good knowledge of these libraries will help any data scientist or machine learning expert handle different types of challenges at a go.

Also read: