Quick Summary:

This blog post lists out the top 10 Python Libraries for Machine Learning, essential for building modern machine learning applications in 2022.

Introduction

If you want to give life to your Machine Learning project, Python is your go-to code. IBM’s machine learning technology considers Python the top-most programming language for ML and AI.

For instance, IBM’s CodeFlare uses Python to simplify integration and scalability and deploy machine learning pipelines.

The Python Machine Learning libraries have specific modules for developers to figure out their Machine Learning requirements. Searching for the best python libraries for machine learning is time-consuming if you haven’t worked closely with ML. A certain level of familiarity is required to import the modules which are suitable for your project.

Here are the 10 Best Python Libraries for Machine Learning you should know in 2022.

Best Python Libraries for Machine Learning

Python Libraries

1. TensorFlow Python:

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.

TensorFlow is one of the best Python libraries for Machine Learning. It is an open-source Python library used for numerical computations.

• Developed by – Google Brain team of Google
• Launched in – 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-

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#  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 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 in – 2005
• Github – https://github.com/numpy/numpy


Features of NumPy Python

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

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Example of NumPy-

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#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 with engineering, math, and science. It has many similarities with the paid tool called MatLab.

SciPy is one of the rich Python Machine Learning libraries for linear algebra, Fourier Transforms, specific functions, image processing, and many more.

• Developed by – Community library project
• Launched in – 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-

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#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 one of the top Python libraries for Machine Learning. It provides the most efficient way to deal with heavily complex data.

It lets you utilize more than a single metric and a top-notch library that provides adequate ML and statistical modeling tools. If you are not using SciKit-Learn when dealing with ML, you’re surely missing something best.

• Developed by – David Cournapeau
• Launched in – 2007
• Github – https://github.com/scikit-learn/scikit-learn
• Written in – Python, 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 dealing with images and text

Well-known Applications using Scikit-Learn

  • Spotify
  • Inria

5. Theano Python

Theano provides tools that define, execute, and optimize mathematical models and expressions with multi-dimensional arrays. To detect and diagnose various error types, utilizing Theano in unit-testing and self-verification is recommended.

Theano is the most versatile Python AI library used for large-scale computing projects but comfortable and specific enough to be implemented by individuals in their projects.

• Developed by – Montreal Institute for Learning Algorithms (MILA), University of Montreal
• Launched in – 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-

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# 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
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    6. Keras Python

    Keras is a very well-known Python Machine Learning library. It is a neural-network API that can run on top of TensorFlow, Theano, or Cognitive ToolKit (CNTK).

    • Developed by – François Chollet
    • Launched in – 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 Machine Learning libraries, providing maximum speed, performance, and flexibility. The main contribution of PyTorch in ML is to escalate the research for accelerating the machine-learning models computationally and making them less expensive.

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


    Features 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 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.

    It was mainly developed for extracting and organizing data. In addition to this, it also offers inbuilt functions and methods to group, combine, and filter datasets.

    • Developed by – Wes McKinney
    • Launched in – 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-

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    # 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

    Days 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 directly related to ML, just like Pandas. Matplolib is considered a convenient tool 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 in – 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

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    #  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

    outout of Python Matpolib

    10. Python mlpack

    mlpack focuses on ease-of-use, scalability, and speed. The key benefit of using the mplack library is to get an extensible, fast, and flexible way of implementing ML algorithms.

    Although meant for C++, bindings for mlpack is available for Go, Julia, Python, and R programming languages. It also features simple command-line programs and C++ classes integrated into large-scale ML solutions.

    • Developed by – Georgia Institute of Technology and the mlpack community
    • Launched in – February 2008
    • Github – https://github.com/mlpack/mlpack
    • Written in – C++


    So, these are some of the best Python Machine Learning libraries that will help take your project to the next level. However, there is a laundry list of Machine Learning libraries in Python, but I have mentioned the most popular and prominent ones.

    Now, as you keep moving deeper into working with Python Machine Learning libraries, you can cherry-pick the right ones per your requirements.

    Why is Python so Popular for Machine Learning?

    Python is the most straightforward language to learn and offers everything beginners, senior developers, and data scientists. Isn’t that great?

    Let’s understand why Python libraries are the most popular for machine learning.

    • 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.

    A single programming language, i.e., Python, covers 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

    You can refer to the below statistics for verification.

    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.

    According to the survey, in the top programming languages 2022, Python remains the most popular programming language.

    popular programming languages in 2022

    I believe now you’re pretty convinced that Python is the most popular language. But, now, the curiosity leads to the why behind this popularity.

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

    Why Should You Choose Python Libraries For Machine Learning

    Python libraries offer a wide range of features and the flexibility to choose, serving almost every project and use case. It holds a significant position in the Ivy league of the most preferred developer-friendly languages. Other advantages are better quality of code and increased productivity.

    As per the below graph from Francois Puget, Python has contributed majorly to the ML environment. That’s why adopting Python libraries for Machine Learning is a default practice.

    Python Contribution

    Here are some reasons that will help you know why everyone chooses the best Python Machine Learning Libraries

    Common Python libraries for Machine Learning

    Libraries are nothing but collections of modules with pre-written code. They 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.

    Here are some of the top benefits of using Python

    • It is a readable language, similar to English, which makes the learning process smoother
    • It fool-proofs the dilemma of choosing the OOPs concept and scripting language
    • Switching to other operating systems is never an issue. You just need to implement small changes, and the OS switching is like a cakewalk.

    Bonus Tip: Notable Python Libraries for Machine Learning

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

    12. Spacy Python: Industrial-strength Natural Language Processing

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

    14. Seaborn Python: Seaborn is a Python data visualization library based on matplotlib that provides high-level informative statistical graphics

    15. Scrapy Python: Scrapy is an open-source and collaborative framework for extracting the data you need from websites

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

    17. OpenCV: OpenCV is a library devoted to image processing computer vision and is an open-source platform.

    Conclusion

    By leveraging these important Python libraries for Machine Learning, you would be able to accomplish your large-scale and individual projects’ needs and requirements.

    I hope that the purpose of landing on this blog has served you well with ML Python libraries. Although, there can still be odds when you cannot choose and implement libraries. It is better and time-saving to reach out to the community or get in touch with us at [email protected] to hire ML developer.

    Frequently Asked Questions (FAQs)

    Python code in run time is contained in a module dedicated to user-specific code. To function in the run time with ease, the package modifies the user-interpreted code

    Rosetta, a privacy-preserving framework based on TensorFlow, is the best privacy-preserving Machine Learning library.

    Anaconda and Miniconda are free, open-source projects that include 1400+ packages in the repository. These versions have become the most known Python distributions widely used in various companies and research laboratories for data science and machine learning. They are free and open-source projects and currently include 1400+ packages in the repository.

    Tesla uses PyTorch to develop full self-driving capabilities for its vehicles, including AutoPilot and Smart Summon. PyTorch is particularly created to scale up from research prototyping to product development.

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