Machine learning is the next big thing in the world of technology.
According to Gartner, a leading IT research firm, 84% of today’s business applications will be shipped with machine learning by 2021. While Python is one of the most popular programming languages used by developers across the globe, it is also one of the best tools for implementing machine learning algorithms.
Google’s TensorFlow was built using Python, and it has become an important part of many businesses, including Google itself!
Let’s take a look at some more Python libraries developers should know for machine learning in 2022.
1. Matplotlib
In the Python machine learning community, Matplotlib is the most used library for plotting.
It creates publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Not only this, but it can be used in Python scripts, the Python and IPython shell (a REPL – read-evaluate-print loop), web application servers, and four graphical user interface toolkits.
You can hire Python developers that use Matplotlib, which is free software released under the GNU General Public License. It offers a wide variety of plot types, including scatter plots, histograms, and bar charts.
The two-dimensional plotting library was developed by John Hunter and Thomas Hunter at Stony Brook University, based on John M. Chambers’ Graphics library for research use in statistical graphics in 1996.
This was done as part of their work developing functional data analysis algorithms at Stony Brook University’s Department of Mathematics. The Hunters made Matplotlib open source because they wanted to see it gain popularity outside their workgroup researching data visualization.
2. Tensorflow
TensorFlow was originally developed by the Google Brain Team and released as open source in November 2015.
Tensorflow is an open-source machine-learning library for numerical computation using data flow graphs. It is a symbolic math library, and its syntax is a superset of Python.
It is one of the top Python machine-learning frameworks because of its powerful features, like allowing for Complete control over the creation of a machine-learning model and a resilient neural network, supporting a large number of extensions and libraries for resolving complicated issues.
TensorFlow provides extremely reliable Python and C++ APIs. It can also offer backward-compatible APIs for other languages, albeit these may be unreliable.
TensorFlow has a flexible design that allows it to run on a wide range of computing platforms, including CPUs, GPUs, and TPUs. TPU is an abbreviation for Tensor processing unit, which is a hardware chip based on TensorFlow for machine learning and artificial intelligence.
Not only this, but it also provides large-scale machine learning for a variety of tasks, via a flexible computation graph that can run on CPUs, GPUs, or TPUs (Tensor Processing Units).
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3. Keras
Keras is a high-level neural networks API written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation.
Keras has several key features:
- Ease of use through its clear and simple API which provides a lightweight frontend to define and build deep learning models.
- Flexibility through the ability to choose either symbolic (as in Theano) or native code execution (with TensorFlow). This means that you can use a CPU or GPU implementation depending on your needs without having to change anything about your model definition.
- Modularity through an easy-to-understand modular architecture that separates model definition from model compilation and allows users easy customization by swapping in different layers libraries such as Lasagne or Blocks in their place without having re-write any code themselves!
Keras employs neural-network construction pieces such as layers, goals, activation functions, and optimizers. Keras also has several tools for working with photos and text images, which are useful when creating Deep Neural Network code.
Keras is one of the most flexible Python machine-learning packages since it includes everything that TensorFlow offers but in a simplified manner. It can also run numerous DL iterations quickly and with complete deployment capabilities.
Besides this, Keras offers convolutional and recurrent neural networks in addition to the basic neural network, further adding to its utility.
4. Scikit-learn
Scikit-learn is an efficient tool for implementing machine learning algorithms and performing data analysis.
It was developed by Sebastian Raschka and Jurgen Van Gael from the University of Leipzig, Germany, with support from the Python Data Science community and NumFOCUS. It is a free software library for machine learning built on top of NumPy, SciPy, and Matplotlib.
The library includes various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN.
Scikit-learn also includes utilities such as functions for plotting data and evaluating models concerning certain criteria (e.g., AUC score). It is a very popular Python module for machine learning.
It provides a high-level interface to several popular machine-learning algorithms, and it’s probably one of the most used Python libraries for machine learning.
To add to this, it also includes interfaces to several visualization libraries like Matplotlib and Seaborn.
5. NumPy
NumPy is a Python package for scientific computing. It provides fast and flexible arrays, functions to operate on these arrays, tools for integrating C/C++ and Fortran code, and an interface to various computational libraries.
NumPy is built on the Numerical Python (Numpy) array object and is part of the SciPy stack. NumPy extends the capabilities of Numeric by adding multi-dimensional arrays and functions that operate on them. NumPy also adds vectorized math functions similar to those found in MATLAB or Mathematica.
With its fast memory access, it allows for quick computations to be performed by taking advantage of parallel processing via multiple processors or cores.
As a result, it is often used in engineering applications such as image processing or mathematical simulations where speed is important.
Machine learning is one of the hot buzzwords in the tech industry, and it is influencing industries and economies across the world. We often hear about how machine learning has been used for a variety of applications, such as autonomous vehicles, natural language processing, computer vision, and more.
Using these python libraries, developers can code for machine learning in a more structured and systematic manner.
Conclusion
So, that’s our list of the top 5 Python libraries you need when you hire Python Developers for machine learning.
There are many other libraries out there, but these are some of the most popular ones that we think you should know about. If you want to try something new, then consider using one of these tools in your next project!
