Python, a computer programming language that was first introduced in 1991, is now one of the most extensively used languages. It’s efficient and simple to learn, and one of its best aspects is the availability of open-source libraries to users. The libraries enable users to select frameworks from which to build new machine learning (ML) models.
People used to execute Machine Learning jobs by manually coding all of the algorithms, mathematical and statistical calculations, and so on. As a result, the processing became time-consuming, tedious, and inefficient. However, with multiple Python libraries, frameworks, and modules, it has become a lot easier and more efficient than in the past. Today, Python is one of the most popular programming languages for this activity, and it has displaced several languages in the business, thanks in part to its extensive library collection.
If you’re new to machine learning, you’ll probably want to start with a Python library (or a few). Here are the best ones for 2022.
What is a Python library?
Python libraries are groups of modules that include useful code and methods, removing the need to develop them from scratch. Tens of thousands of Python libraries are available to assist machine learning engineers, as well as experts working in data science, data visualization, and other fields.
Python is the chosen machine learning language because its syntax and commands are similar to English, making it efficient and simple to learn. Python, as compared to C++, R, Ruby, and Java, remains one of the simplest languages, allowing for accessibility, versatility, and portability. It can run on almost any operating system or platform.
Best Python Libraries for Machine Learning
Here is a list of the best Python libraries that are used in Machine Learning:-
- Scikit-learn
- PyTorch
- Keras
- Theano
- TensorFlow
- Numpy
- Scipy
- Pandas
- Matplotlib
Without further Ado let’s get into each of the python libraries for Machine Learning
1. Scikit-learn
Scikit-learn is a well-known machine learning package based on NumPy and SciPy. It supports the majority of traditional supervised and unsupervised learning techniques and may also be used for data mining, modeling, and analysis. Scikit-straightforward learns design makes it an accessible library for individuals new to machine learning.
Python3
# Python script using Scikit-learn
# for Decision Tree Classifier
# Sample Decision Tree Classifier
from sklearn import datasets
from sklearn import metrics
from sklearn.tree import DecisionTreeClassifier
# load the iris datasets
dataset = datasets.load_iris()
# fit a CART model to the data
model = DecisionTreeClassifier()
model.fit(dataset.data, dataset.target)
print(model)
# make predictions
expected = dataset.target
predicted = model.predict(dataset.data)
# summarize the fit of the model
print(metrics.classification_report(expected, predicted))
print(metrics.confusion_matrix(expected, predicted))
For more details refer to the documentation.
2. PyTorch
PyTorch is an open-source Python machine learning library based on the Torch C programming language framework. It is mostly utilized in machine learning applications involving natural language processing or computer vision. PyTorch is well-known for its lightning-fast execution of massive, dense data sets and graphs.
Python3
# Python program using PyTorch
# for defining tensors fit a
# two-layer network to random
# data and calculating the loss
import torch
dtype = torch.float
device = torch.device("cpu")
# device = torch.device("cuda:0") Uncomment this to run on GPU
# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10
# Create random input and output data
x = torch.random(N, D_in, device=device, dtype=dtype)
y = torch.random(N, D_out, device=device, dtype=dtype)
# Randomly initialize weights
w1 = torch.random(D_in, H, device=device, dtype=dtype)
w2 = torch.random(H, D_out, device=device, dtype=dtype)
learning_rate = 1e-6
for t in range(500):
# Forward pass: compute predicted y
h = x.mm(w1)
h_relu = h.clamp(min=0)
y_pred = h_relu.mm(w2)
# Compute and print loss
loss = (y_pred - y).pow(2).sum().item()
print(t, loss)
# Backprop to compute gradients of w1 and w2 with respect to loss
grad_y_pred = 2.0 * (y_pred - y)
grad_w2 = h_relu.t().mm(grad_y_pred)
grad_h_relu = grad_y_pred.mm(w2.t())
grad_h = grad_h_relu.clone()
grad_h[h < 0] = 0
grad_w1 = x.t().mm(grad_h)
# Update weights using gradient descent
w1 -= learning_rate * grad_w1
w2 -= learning_rate * grad_w2
For more details refer to the documentation.
3. Keras
It provides many inbuilt methods for groping, combining, and filtering data.
Keras is a Python library that is specifically built for creating neural networks for ML models. It can train neural networks on top of Theano and TensorFlow. Keras is adaptable, portable, and user-friendly, and it integrates readily with a variety of functions.
For more details refer to the documentation.
4. Theano
Theano is a Python library designed primarily for machine learning that focuses on numerical computing. It has the capacity to enhance and assess mathematical models and matrix computations that produce ML models from multi-dimensional arrays. Developers and programmers who specialize in machine learning and deep learning nearly exclusively utilize Theano.
Python3
# Python program using Theano
# for computing a Logistic
# Function
import theano
import theano.tensor as T
x = T.dmatrix('x')
s = 1 / (1 + T.exp(-x))
logistic = theano.function([x], s)
logistic([[0, 1], [-1, -2]])
For more details refer to the documentation.
5. TensorFlow
TensorFlow is a prominent open-source library for high-performance numerical computing created by Google’s Brain team. As the name implies is a framework for defining and conducting tensor-based calculations. It can train and run deep neural networks, which can be utilized to create a variety of AI applications. TensorFlow is frequently used in deep learning research and development.
Python3
# Python program using TensorFlow
# for multiplying two arrays
# import `tensorflow`
import tensorflow as tf
# Initialize two constants
x1 = tf.constant([1, 2, 3, 4])
x2 = tf.constant([5, 6, 7, 8])
# Multiply
result = tf.multiply(x1, x2)
# Initialize the Session
sess = tf.Session()
# Print the result
print(sess.run(result))
# Close the session
sess.close()
For more details refer to the documentation.
6. NumPy
NumPy is a popular Python library for multi-dimensional array and matrix processing because it can execute a wide range of mathematical operations. NumPy’s ability to handle linear algebra, Fourier transform, and other functions makes it excellent for machine learning and artificial intelligence (AI) projects, allowing users to change the matrix to easily increase machine learning performance. NumPy is faster and easier to use than most other Python packages.
Python3
# Python program using NumPy
# for some basic mathematical
# operations
import numpy as np
# Creating two arrays of rank 2
x = np.array([[1, 2], [3, 4]])
y = np.array([[5, 6], [7, 8]])
# Creating two arrays of rank 1
v = np.array([9, 10])
w = np.array([11, 12])
# Inner product of vectors
print(np.dot(v, w), "\n")
# Matrix and Vector product
print(np.dot(x, v), "\n")
# Matrix and matrix product
print(np.dot(x, y))
For more details refer to Numpy.
7. SciPy
SciPy is a popular Python library among Machine Learning aficionados since it includes modules for optimization, linear algebra, integration, and statistics. There is a distinction to be made between the SciPy library and the SciPy stack. SciPy is one of the essential packages that comprise the SciPy stack. SciPy may also be used to manipulate images.
Python3
# Python script using Scipy
# for image manipulation
from scipy.misc import imread, imsave, imresize
# Read a JPEG image into a numpy array
img = imread('D:/Programs / cat.jpg') # path of the image
print(img.dtype, img.shape)
# Tinting the image
img_tint = img * [1, 0.45, 0.3]
# Saving the tinted image
imsave('D:/Programs / cat_tinted.jpg', img_tint)
# Resizing the tinted image to be 300 x 300 pixels
img_tint_resize = imresize(img_tint, (300, 300))
# Saving the resized tinted image
imsave('D:/Programs / cat_tinted_resized.jpg', img_tint_resize)
If scipy.misc import imread, imsave,imresize does not work on your operating system then try below code instead to proceed with above code
!pip install imageio
import imageio
from imageio import imread, imsaveFor more details refer to the documentation.
8. Pandas
Pandas is a well-known Python data analysis toolkit. It has nothing to do with Machine Learning. We are all aware that the dataset needs to be ready before training. Pandas are useful in this situation because it was created specifically for data preprocessing and extraction. It provides high-level data structures as well as a comprehensive range of data analysis capabilities. It includes numerous built-in methods for grouping, merging, and filtering data.
Python3
# Python program using Pandas for
# arranging a given set of data
# into a table
# importing pandas as pd
import pandas as pd
data = {"country": ["Brazil", "Russia", "India", "China", "South Africa"],
"capital": ["Brasilia", "Moscow", "New Delhi", "Beijing", "Pretoria"],
"area": [8.516, 17.10, 3.286, 9.597, 1.221],
"population": [200.4, 143.5, 1252, 1357, 52.98] }
data_table = pd.DataFrame(data)
print(data_table)
For more details refer to Pandas.
9. Matplotlib
Matplotlib is a well-known Python data visualization package. It is not directly related to Machine Learning, like Pandas. Pandas are very useful when a programmer wishes to visualize data patterns. It is a 2D charting package that may be used to create 2D graphs and charts. A tool called pyplot simplifies plotting for programmers by providing options for controlling line styles, font characteristics, formatting axes, and so on. It includes a variety of graphs and plots for data visualization, such as histograms, error charts, bar charts, and so on.
Python3
# Python program using Matplotlib
# for forming a linear plot
# importing the necessary packages and modules
import matplotlib.pyplot as plt
import numpy as np
# Prepare the data
x = np.linspace(0, 10, 100)
# Plot the data
plt.plot(x, x, label ='linear')
# Add a legend
plt.legend()
# Show the plot
plt.show()
For more details refer to the documentation.
Conclusion: Let’s Wrap
It is clear that python libraries for machine learning are becoming increasingly popular. There are many different python libraries available, and each one has its own advantages and disadvantages.
One of the main advantages of using a python library is that it makes the process of using machine learning much easier. The library takes care of all the necessary calculations for you, so all you need to do is provide the input data and the library will do the rest.
However, there are also some disadvantages to using a library. For example, some libraries are more complex than others and may require more expertise to use. Additionally, some libraries may only be available on certain platforms, so you may need to make sure that your machine is compatible with the library before you begin using it.
Overall, python libraries for machine learning are a great way to simplify the process of using machine learning and make it easier for you to get started.
Let me know in the comments which python library you like the most for machine learning.
Share This Post, Help Others & Learn Together!