Top Python Libraries for Machine Learning in 2021

Whenever the word libraries we hear, the very first thing that comes to our mind is books nicely placed on shelves. We visit and compare different sections based on areas of interest to find and pick up the best book, right?

Now let’s use this analogy and relate it with the scenario of Python libraries for machine learning. As libraries have various categories of books, Python libraries list have modules. Software programmers figure out their Machine Learning requirements and accordingly search for the best machine learning library in Python to import the suitable modules.

And this ‘search’ is kind of hectic when you don’t have any prior knowledge of all Python libraries and their contribution to Machine Learning. So, I thought of lessening the ‘search struggle’ of fellow Python developers by exploring each Python machine learning library. Without further ado, let’s have a look at the list of ML libraries in python.

Introduction: Python Machine Learning Library

Machine learning has progressed drastically over the past few years, making a significant contribution to the AI industry. And the most considerable programming language for Machine Learning is believed to be Python. You might have heard about various other programming languages (Python, R, Julia, Java) offering efficient ML concepts, but Python has been usually referred to as the most proficient language. And the reason behind the popularity of Python is the immense usage of Python Libraries for areas like Deep Learning, Artificial Intelligence, and Machine Learning.

Here is a laundry list of questions that people are looking for all over when it comes to choose Python Machine Learning library;

• What are the most popular Python-based ML libraries?
• What are the essential libraries for ML in python?
• What are the modules and packages in Python?
• What are important Python libraries for machine learning?
• Which Python library is the best for privacy-preserving ML?
• What are some good Python packages for machine learning?
• Which Python library is best for ML?
• Do top-tier ML experts make their own ML algorithm or do they use Python libraries or R modules?

Let’s proceed and explore top Python libraries for Machine Learning in 2021.

Why is Python language so popular in ML?

Python is the most straightforward language to learn and offers everything needed to beginners, senior developers, and data scientists. Isn’t that great? One programming language covering most of the aspects of the IT world. Python has made the struggle of data scientists a lot less than before. And that is why you can see the below graph and understand how consistently Python has been a winner compared to other languages.

Python Popularity Chart

Based on the numbers of searches in Google, Python language topped with 29.99% in 2020, followed by Java with 19.1% and Javascript with 8.2. As per the survey, in the list of top programming languages 2020, Python remains as the most popular programming language in 2020. You can refer to the below statistics for verification.

I believe now you’re pretty convinced with the fact that Python is the most popular language. But, now, the curiosity leads to the Why part behind this popularity. Let’s dive deeper and seek answers.

• Large and Supportive Community
• Simple and Easy to learn and code
• It covers everything from development to deployment
• Compatibility with Hadoop
• Commendable processing speed with lesser lines of code

We have overviewed the popularity and the reason behind Python popularity. Now, let’s have a look at the best Python libraries for ML.

Why Should You Choose Python Libraries for Machine Learning?

As per the below graph from Francois Puget, Python has contributed majorly to ML. That’s why Python ML library is a default practice.

Here are some reasons that will help you know why everyone chooses Python Libraries for ML.,

➜ Common libraries in python for ML –

Libraries are nothing but collections of modules with pre-written code and can be easily imported and used by developers to implement any functionality. ML requires regressive and continuous data processing, and Python fulfills this requirement by accessing, handling, and transforming data.

Python linked list library list;

• NumPy and SciPy for Scientific Computation
• BeautifulSoup and Scrapy for Data Mining
• Pandas, Matplotlib, Seaborn, and Plotly for Data Exploration and Visualization
• Scikit learn, PyCaret, Keras, PyTorch, and TensorFlow for Machine Learning.

We will further discover ML Python libraries in-detail.

➜ Python code resembles everyday English language, which makes the learning process a lot easier. It saves us from the dilemma of choosing the OOPs concept and scripting language. Its simple syntax adds flexibility to the developer’s mindset.

➜ Switching to other operating systems is never an issue if you’re using Python for ML. You just need to implement small changes, and then the OS switching is a piece of cake.

Best Python Libraries for Machine Learning

Here is the list of top 9 Python libraries for machine learning in 2021.

1. TensorFlow Python

In the list of best Python libraries for Machine Learning TensorFlow is at the top. If you have probably heard, implemented or planning to install TensorFlow in Machine Learning, then let me get you through. TensorFlow is an open-source Python library used for numerical computations. As per the name, TensorFlow defines and runs the series of operations on tensors. Tensors are nothing but N-dimensional matrices representing your data. TensorFlow runs and trains neural networks, which are further used in AI applications.

Developed by – Google Brain team of Google
Launched year -2015
Github – https://github.com/tensorflow/tensorflow
Written in – Python, CUDA, and C++

Features of TensorFlow-

✦ Visualizing minute details of the graph is possible, which was hard to achieve with Numpy or Scikit.
✦ Provides flexibility and modularity.
✦ Can be trained smoothly on CPU and GPU
✦ Offers pipeline to train multiple neural networks and GPUs
✦ Large Community
✦ Open Source

Well-known Applications using TensorFlow Python-

✦ Google Voice Search
✦ Google Photos

Example of TensorFlow Python-

#  Program using TensorFlow
#  for adding two arrays
  
import tensorflow
  
# Initializing constants
x = tensorflow.constant([2, 4, 6])
y = tensorflow.constant([1, 3, 5])
  
# Addition
res = tensorflow.add(x, y)
  
# Initializing Session
sess = tensorflow.Session()
  
# Result
print(sess.run(res))
  
# Closing Session
sess.close()

Output

[3, 7, 11]

2. NumPy Python

NumPy (NUMerical PYthon) is a library that is used to process the Python numpy array. It consists of highly complex mathematical functions that make NumPy powerful when dealing with substantial multidimensional matrices and arrays. It is well-known for handling linear algebra and Fourier series transformations. A library like TensorFlow utilizes NumPy at the backend to manipulate tensors.

Developed by – Travis Oliphant
Launched year -2005
Github –https://github.com/numpy/numpy

Features of NumPy Python-

✦ Easy to use and interact
✦ Supports and makes the mathematical operations and calculations very simple
✦ Having a large community of programmers
✦ Manages garbage collection as it gives a dynamic structure
✦ Enhances performance

Example of NumPy-

#Sorting 2-D array
import numpy 

#Initializing 2-D array
arr = numpy.array([[11, 3], [21, 14]])

#Sorting array and printing output
print(numpy.sort(arr))

Output

[[3, 11], [14, 21]]

3. Python SciPy

SciPy ( SCIentific PYthon) is an open-source Python library that mainly focuses on scientific computing, which is concerned majorly with engineering, math, and science. It has many similarities with the paid tool called MatLab. SciPy is one of rich Python modules for machine learning for linear algebra, Fourier Transforms, some specific functions, image processing, and many more.

Developed by – Community library project
Launched year -2001
Github – https://github.com/scipy/scipy
Written in – Python, C++, C, and Fortran

Features of Python SciPy-

✦ Efficiently uses NumPy arrays to generate data structures.
✦ Supports numpy.lib.scimath
✦ Efficiently manages 1-D polynomials in two different systems.
✦ Provides faster computational power

Example of Python SciPy-

#Finding Cubic Root 
from scipy.special import cbrt 

#Initializing 
res = cbrt([343, 1331])

#Print output
print(res)

Output

[7, 11]

4. Python Scikit-Learn

Scikit-Learn is considered to be one of the best Python libraries for Machine Learning. It provides the most efficient way when it comes to deal with heavily complex data. It lets you utilize more than a single metric. It is the top-notch library that provides adequate tools for ML and statistical modeling. If you are not using SciKit-Learn when dealing with ML, you’re surely missing something best.

Developed by – David Cournapeau
Launched year -2007
Github – https://github.com/scikit-learn/scikit-learn
Written in – Python, C, C++, and Cyhton

Features of Scikit-Learn-

✦ By using various methods, it observes the effectiveness of supervised models.
✦ Contains a rich and massive set of potent algorithms
✦ Advantageous when we have to deal with images and text

Well-known Applications using Scikit-Learn-

✦ Spotify
✦ Inria

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5. Theano Python

Machine Learning mainly concerns mathematical and statistical algorithms. The ML libraries, Theano provides tools that involve defining, executing, and optimizing mathematical models and expressions with multi-dimensional arrays. For detecting and diagnosing various error types, it is recommended to utilize Theano in unit-testing and self-verification. Theano is a versatile Python library used for large-scale computing projects but comfortable and straightforward enough to be implemented by individuals in their projects.

Developed by – Montreal Institute for Learning Algorithms (MILA), University of Montreal
Launched year -2007
Github – https://github.com/Theano/Theano
Written in – Python, CUDA

Features of Theano Python-

✦ It uses NumPy arrays in the functions compiled with Theano.
✦ Undergoes computations quicker than CPU
✦ Efficiently recognizes and diagnoses errors.

Example of Theano Python-

# Program to computing a Logistic 
# Function
  
import theano as t
import theano.tensor as Th
a = Th.dmatrix('a')
b = 1 / (1 + Th.exp(-a))
logistic = t.function([a], b)
logistic([[0, 1], [-1, -2]])

Output

array([[0.5, 0.73105858], [0.26894142, 0.11920292]])

Well-known applications using Theano-

✦ Zetaops
✦ Vuclip

6. Keras Python

Keras is a very well-known library contributing immensely to Machine Learning. It is a neural-network API that has the potential of running on top of TensorFlow, Theano, or Cognitive ToolKit (CNTK).

Developed by – François Chollet
Launched year -2015
Github – https://github.com/keras-team/keras
Written in – Python

Features of Keras Python-

✦ Works similarly on CPU and GPU
✦ Mostly supports all the Neural Networks models.
✦ Flexible and easy to utilize
✦ Supports multi-backend
✦ Contains modular architecture

Well-known applications using Keras-

✦ Uber
✦ Netflix

7. Python PyTorch

Python PyTorch is one of the largest Python libraries for Machine Learning, providing maximum speed, performance, and flexibility. The main contribution of PyTorch in ML is to escalate the research for fastening the machine-learning models computationally and make them less expensive.

Developed by – Facebook’s AI Research lab
Launched year -2016
Github – https://github.com/pytorch/pytorch
Written in – Python, CUDA, and C++

FFeatures of PyTorch

✦ Can be efficiently utilized with other libraries and Python machine learning packages
✦ Offers immense flexibility
✦ Performance optimization in both research and production environments
✦ Provides robust ecosystem

Well-known applications using PyTorch

✦ Apple
✦ Samsung Electronics

8. Python Pandas

Pandas is a well-known library that is used for extensive data analysis. We all know that preparing a dataset before training is the principal activity. Python Pandas provides high-level tools and data structures in this scenario as it was mainly developed for extracting and organizing data. In addition to this, it also offers inbuilt functions and methods to grop, combine, and filter datasets.

Developed by – Wes McKinney
Launched year – 2008
Github – https://github.com/pandas-dev/pandas
Written in – Python, Cython, and C

Features of Python Pandas-

✦ Best tools and data structures for data analysis and manipulation
✦ Provides support to multiple operations like
✦ Aggregations
✦ Visualizations
✦ Concatenations
✦ Iteration
✦ Sorting

Example of Python Pandas-

# Program to implement 
# Pandas DataFrame  

import pandas 
 
data_set = {
“words_written” : [1450, 3450, 1340]
“hours” : [1, 2, 1]  
}
res = pandas.DataFrame(data_set, index = [“Monday”, “Tuesday”, “Wednesday”])
 
print(res)

Output

words_written  hours
Monday         1450        1 
Tuesday        3450        2
Wednesday      1340        1

9. Python Matplotlib

Matplolib is famous for data-visualization. It is also not connected directly to ML, just like Pandas. It is considered a convenient tool that can be utilized when developers visualize data patterns. The primary usage of Matpolib is to produce 2-dimensional graphs and plots. Pyplot module makes plotting more convenient because it offers features and tools for controlling line styles, font properties, and many more.

Developed by – Michael Droettboom, et al.
Launched year -2003
Github –https://github.com/matplotlib/matplotlib
Written in – Python

Features of Python Matplotlib-

✦ Provides comprehensive and robust tools for plotting
✦ Allows to analysis data in a detailed way

Example of Python Matpolib-

#  Program to form a linear plot
  
# import packages & modules
import matplotlib.pyplot as matplot
import numpy 
  
# Initializing data
a = numpy.linspace(0, 4, 10)
  
# Plotting data
matplot.plot(a, a, label ='linear')
  
# Adding legend
matplot.legend()
  
# Showing plot
matplot.show()

Output

So, these are some of the most popular Python libraries for Machine Learning that will help you with ML. However, there are laundry list of Python libraries, but I have mentioned the most popular and prominent ones. Now, as you have a high-level knowledge of most of the Python libraries, you can choose the correct one as per your requirements. If you are looking for a helping hand to get the job done, leverage our Python developers’ expertise.

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Bonus Tip: Notable Python Libraries for Machine Learning

#10 NLTK Python: NLTK is one of the leading platforms for building Python programs to work with human language data.

#11 Spacy Python: Industrial-strength Natural Language Processing<.p>

#12 Gensim Python: Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large corpora.

#13 Seaborn Python: Seaborn is a Python data visualization library based on matplotlib, provides high-level informative statistical graphics.

#14 Scrapy Python: An open-source and collaborative framework for extracting the data you need from websites.

#15 LightGBM Python: LightGBM is a gradient boosting framework that uses tree-based learning algorithms.

Conclusion

I hope that the purpose of landing on this blog has served you well with ML Python libraries. By leveraging these important Python libraries for Machine Learning, you would be able to accomplish your large-scale and individual projects’ needs and requirements. Although, there can still be odds when you’re unable to choose and implement libraries. It is better and time-saving to take the community’s help or hire dedicated Python developer who are well-versed and have Machine Learning insights in such situations.