Python is one of the most commonly used languages, having its applications in data science, scripting, and creating back-end RESTful APIs with frameworks such as Django and Flask. While having its domain in web development, most companies prefer using Python for their data science and tasks relating to artificial intelligence.Â
Python’s wealth of open-source libraries is one of its most useful yet underappreciated characteristics. They may be used for anything from data science and visualization to picture and data modification. However, in recent years, some Python libraries have established an irrefutable presence in machine learning (ML) and deep learning (DL) domains.
Python is extremely adaptable and flexible, allowing it to be used with other languages as needed, in addition to its low-level entry and specialized code libraries. It also works on practically every operating system and platform available.
It includes packages that drastically reduce the amount of labor necessary to create complex neural networks and machine learning techniques. Furthermore, Python can also be used as an Object Oriented Programming language, which is required for effective data utilization and classification—a critical component of any machine learning process.
Why do developers and data scientists prefer Python?
Python provides code that is concise and readable. While artificial intelligence and machine learning rely on complicated algorithms and varied workflows, Python’s ease allows developers to create dependable systems. Developers may devote their entire attention to addressing an ML problem rather than worrying about the technical subtleties of the language.
Python is also intriguing to many developers since it is simple to learn. Python code is intelligible by humans, making it easier to develop machine learning models.
The ten best Python libraries for Machine Learning and Deep Learning
1. TensorFlow
A Python library that excels in differentiable programming is TensorFlow. TensorFlow is free and open-source. Developing machine learning, deep learning models, and neural networks are made simple for both novices and experts by the library’s unimaginable plethora of tools and materials. The flexible design and foundation of TensorFlow enable it to function on various computing systems, including CPU and GPU.Â
TensorFlow enables you to immediately train and test your ML models using its built-in tools and may be used to apply learning algorithms in Deep Learning and Neural Networks. TensorFlow is not just for desktop computers. You may also use it on servers and mobile devices to build and train intelligent models.
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2. Keras
Keras is a Python open-source framework for creating and analyzing neural networks in deep learning and machine learning models. It can run on top of both TensorFlow and Theano, allowing you to start training neural networks with just a few lines of code, which, compared to its parent frameworks, is minuscule. The Keras library is versatile and scalable suitable for both beginners and experienced programmers. It also provides a fully functional model for building neural networks by integrating targets, optimizers, layers, and activation functions.
The Keras framework is adaptable and portable, allowing it to run in various contexts and on both CPUs and GPUs. It enables quick and efficient prototyping, research, data modeling, and visualization.
3. NumPy
NumPy is a prominent open-source numerical Python library. It can conduct a wide range of mathematical computations on matrices and arrays. It’s one of the most famous scientific Python libraries, and data scientists frequently use it for data analysis. Its ability to analyze multi-dimensional arrays and handle linear algebra and Fourier transformations makes it excellent for ML applications.
NumPy arrays demand substantially less storage space than conventional Python lists. They are also considerably quicker and easier to operate on than the latter. NumPy lets you alter the data in the matrices by transposing and reshaping it. Numpy’s characteristics, when combined with other Python libraries, allow you to boost your machine learning model’s performance easily.
4. Pandas
Pandas is one of the most widely used Python libraries for machine learning. It is basically a Python framework for data research and analysis that allows developers to create appealing and flawless high-level data representations. Pandas, built on NumPy, is in charge of preparing data sets and points for machine learning. Pandas employs two types of data structures: 1-D (series) and 2-D (DataFrame), enabling Pandas to be utilized in various fields, from science and statistics to finance and engineering.
The Pandas library is adaptable and can work with other scientific libraries such as SciPy, Scikit-learn, etc., and numerical libraries such as NumPy. Due to its highly descriptive, fast, and compliant nature, Pandas is very simple to use. Pandas allow a user or a data scientist to change data functionality by combining, merging, and re-indexing it with a few keystrokes.
5. SciPy
SciPy is a well-known Python library among artificial intelligence aficionados since it includes pre-built modules for linear algebra, optimizations, mathematical integration, and statistics.
SciPy heavily relies on NumPy for array manipulation subroutines and incorporates all of NumPy’s features. SciPy is suited for image processing and includes quasi-high-level mathematical operations with basic processing features. It’s simple to use and quick to implement. It also has high-level commands for visualizing and manipulating data.
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6. Scikit-Learn
Scikit-learn is a free Python AI library that is generally considered a straight extension of SciPy. It is based on NumPy and SciPy. It was created primarily for data modeling and building supervised and unsupervised machine learning algorithms.
Scikit-learn is both beginner and user-friendly due to its simple, straightforward, and consistent interface. Although the usage of scikit-learn is constrained since it specializes solely in data modeling, it does a good job of allowing users to alter and exchange data in whatever way they choose.
7. Theano
Theano is a popular Python package for optimizing mathematical operations using multi-dimensional arrays. It is accomplished through optimizing GPU and CPU usage. Theano is a tremendously robust library that has long been used in scientific research where there is a massive scale of computations, yet it is basic enough that anyone may use it for their projects.
8. PyTorch
PyTorch is an open-source Python machine learning library based on the Torch C programming language framework. PyTorch can be integrated with other Python libraries like NumPy. It can generate computational graphs that may be altered at any moment while the Python application runs. It is mainly used in the domains of NLP(Natural Language Processing) and CV(Computer Vision).
9. Matplotlib
Matplotlib is a well-known Python data visualization library. It is not directly connected to data handling libraries like Pandas. It is 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 in the Matplotlib library simplifies plotting for programmers by providing options for controlling the various mechanisms for visualizing the data.
10. OpenCV
OpenCV is a python neural network library. It is a collection of numerous programming functions that can be used in real-time computer vision systems. It can identify faces and objects and even recognize handwriting. The library allows users to interpret various visual inputs from the picture and video data.
Conclusion
Python libraries are groups of code and functions that reflect the heart of Python. They function by being imported into the core Python architecture and linking to other Python functions. This allows a user or a data scientist to call and utilize the new functions without having to program them.
 If you want to work in the sectors of data science and Python with your talents, you will need adequate hands-on experience. Engaging in projects will provide you with the necessary practical skills and significantly strengthen your résumé. If you are a newbie seeking a dependable course to begin learning Data Science and Business Analytics, you may enroll in upGrad’s Professional Certificate in Data Science and Business Analytics.
How long does it take for a beginner to learn data science with Python?
It usually takes a beginner three months to a full year to reach an intermediate level in learning data science with Python.
What is the best Python library to work with the data science domain?
There is no best Python library for machine learning, but specific libraries are better than others in particular domains.
What is the duration of the Professional Certificate in Data Science and Business Analytics course by upGrad?
The Professional Certificate in Data Science and Business Analytics course will run for a total period of 7 months.
