Best Python libraries for Machine Learning.

Machine learning has been one of the hottest topics in the tech market. In general, typically it is used to solve various types of life problems. Python is one of the best languages you can use to learn machine learning techniques. Python is considered one of the fastest-growing programming languages as it is simple to use i.e. its syntax,  it has libraries that ensure you can find a solution for every existing problems, implementation is smooth and increases productivity by reducing coding and debugging times.

Let’s discuss about the most popular libraries .

  1. Numpy : Numpy stands for Numerical Python. It is a library used for working with arrays. Arrays are very frequently used in data science, where speed and resources are very important ,this helps in working it faster.

Once NumPy is installed, import it in your applications by adding the import keyword.

import numpy

Example:

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr)

2. Pandas : Pandas has a reference to both Panel Data, and Python Data Analysis. It is used to analyze data i.e. used for working with data sets.It can also cleans data and analyzes big data by giving conclusion based on statistical theories. To store big data we use CSV file and also big data sets are often stored, or extracted as JSON. Once Pandas is installed, import it in your applications by adding the import keyword.

import pandas

Example :

import pandas as pd
a = [1, 7, 2]
myvar = pd.Series(a)
print(myvar)

3.  Matplotlib : It is used for data visualization and for making 2D plots from data in arrays. It is open source and we can use it freely. Importing Matplotlib i.e. once Matplotlib is installed, import it in your applications by adding the import module statement .

import Matplotlib

Pyplot: Most of the Matplotlib utilities lies under the pyplot submodule, and are usually imported under the ‘plt’.

import matplotlib.pyplot as plt

Example:

import matplotlib.pyplot as plt
import numpy as np
xpoints = np.array([0, 6])
ypoints = np.array([0, 250])
plt.plot(xpoints, ypoints)
plt.show()

4. SciPy: SciPy stands for scientific python. This library function contains different types modules for optimization, integration, linear algebra and statistics. It is also used for image manipulation.

Let’s import SciPy library.

import SciPy

Let’s assume we have imported constants module from SciPy import constants

Example:

from SciPy import constants
print(constants.liter)

5. Scikit-learn: It is built using two libraries of python i.e. Numpy and SciPy . It helps to select efficient tools for statistical modeling including classification, regression, clustering and  dimensionality reduction.

Let’s import scikit-learn library.

impot sklearn

Example:

from sklearn import datasets
iris= datasets.load_iris()
print(iris.data.shape)

6. Tensorflow: It is one of the most popular library used in machine learning. It is a math library used for building and training neural networks to detect patterns and correlations, similar to human learning and reasoning and also used for fast numerical computing. It is used both in research and development and in production systems.

Let’s import tensorflow library.

import tensorflow as tf

Example:

const1 = tf.constant([[1,2,3], [1,2,3]]);
const2 = tf.constant([[3,4,5], [3,4,5]]);
result = tf.add(const1, const2);
with tf.Session() as sess:
output = sess.run(result)
print(output)

7. Keras : It is one of the important library used for implementing deep learning models which makes the research and development easy and fast. Many leading organizations like Google, Square, Netflix, Huawei and Uber are currently using Keras.

When you install tensorflow, keras is installed along with it. Thus you can import keras from tensorflow:

from tensorflow.keras import Sequential

8. PyTorch : This library also plays a vital role in machine learning by python. The main feature of the PyTorch is it is n-dimensional Tensor, similar to numpy but can run on GPUs and can do Automatic differentiation for building and training neural networks. It has a choice of tools and libraries which supports Computer Vision, Natural Language Processing(NLP) and many more ML programs.

Let’s import PyTorch library.

import PyTorch

Example:

import numpy as np
import torch
# initializing a numpy array
a = np.array(1)
# initializing a tensor
b = torch.tensor(1)
print(a)
print(b)

Conclusion:

From this blog we can know the importance of the libraries which play a major role in machine learning and also we can conclude that python is one of the fastest growing programming language.

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