• Developed by – Google Brain team of Google • Launched in – 2015 • Github – https://github.com/tensorflow/tensorflow • Written in – Python, CUDA, and C++ |
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Quick Summary:
This blog post lists out the top 10 Python Libraries for Machine Learning, essential for building modern machine learning applications in 2023.
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 Machine Learning python library has 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 2023.
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++ |
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Features of TensorFlow
Well-known Applications using TensorFlow Python
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]
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 |
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Features of NumPy Python
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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]]
SciPy (SCIentific PYthon) is an open-source Python for machine learning 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 |
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Features of Python SciPy
Example of Python SciPy-
#Finding Cubic Root from scipy.special import cbrt #Initializing res = cbrt([343, 1331]) #Print output print(res)
Output
[7, 11]
Scikit-Learn is considered one of the top libraries used in 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 |
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Features of Scikit-Learn
Well-known Applications using Scikit-Learn
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 |
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Features of Theano Python
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
Keras is a very well-known Machine Learning library python. 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 |
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Features of Keras Python
Well-known applications using Keras
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++ |
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Features of PyTorch
Well-known applications using PyTorch
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 |
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Features of Python Pandas
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
Days | Words_written | Hours |
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Monday | 1450 | 1 |
Tuesday | 3450 | 2 |
Wednesday | 1340 | 1 |
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 |
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Features of Python Matplotlib
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
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++ |
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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.
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.
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 2023, Python remains the most popular programming language.
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.
Libraries are nothing but collections of modules with pre-written code. They can be easily imported and used by developers to implement any functionality.
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.
ML requires regressive and continuous data processing, and Python fulfills this requirement by accessing, handling, and transforming data. Python linked list library list:
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 has become a default practice.
Simplicity: Python language is a readable language, almost similar to English, which makes the learning curve smoother. In addition to that, the language provides simplicity because of its concise structure and syntax.
Massive Reliable Support: Python being the pioneer in Machine Learning, its community has immense and trustworthy support. Having such a strong and reliable backbone cultivates more trust for using Python libraries in your ML and Data science projects. It also fool-proofs the dilemma of choosing the OOPs concept and scripting language
Fast Development: Implementing python libraries is definitely not a tedious and hectic job. A python developer won’t be exhausted or stuck in the loop if they are adopting python libraries. Definitely, for that, your basics should be clear but it won’t take long tiring hours of coding, rather it’d be brainstorming activity. Moreover, if there’s a scenario of switching to other operating systems, it won’t be a problem. You just need to implement small changes, and the OS switching is like a cakewalk.
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.
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.
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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