Python is a multi-paradigm programming language that was initially developed as a hobby by Guido van Rossum. It was derived from programming languages like Modula 3m ABC, Algol-68 & small talk. Instead of inspiration from rather popular snake, its name “Python” was inspired by Rossum’s favorite TV show called Monty Python’s Flying Circus.
Today, it has become one of the most popular programming languages in the world with a wide range of applications varying from data science to machine learning to web development, IoT, Image Processing, Android Applications, Rapid Development Tools & others.
Primarily, this programming language was developed as a more straightforward, concise, high-level and easy-to-understand alternative to other major programming languages like Perl, Java or C. It is also an open-source which allows anyone to download and use it freely, and its code can be accessed or modified as per the requirements.
Moreover, it has also become the official programming language of Google. Various other major companies are also using it for different purposes. For instance, Netflix uses Python for data science to understand user viewing patterns and behavioral drivers. This helps them to predict and suggest relevant shows to its users with unbelievable accuracy. Spotify is again using it for data science & machine learning applications for recommending the right music to its users. Even Amazon, Walmart and Central Intelligence Agency are using this programming language for their own interests.
What Makes This Programming Language so Important for ML?
It is a high-level, general-purpose & object-oriented programming language that can be understood without any difficulties. It has become popular mainly because of its ease of use, simplicity, accessibility and open source licensing. Furthermore, its vast community and great support from all over the world help anyone to create or access hundreds of packages, tutorials and sample programs.
Other than these, its extensive support of libraries, user-friendly data structures, speed and productivity makes it the option of choice for the majority of the data scientists and machine learning experts throughout the world.
Python Programming Language for ML
Though artificial intelligence & machine learning is a relatively new field but is spreading across various industries at a significantly high rate. It is an evolving technology that has quickly become mandatory for various organizations, companies and programmers to know and use. It is a recommended option for data analysis and processing of data in order to provide more accurate insights and predictions about users for enhancing business efficiency, lower production cost and high productivity.
In simple terms, machine learning allows any computer to perform a task without any explicit programming. It helps them to learn & automate the tasks accordingly by analyzing different patterns and recognizing errors. Currently, the machine learning algorithm has become the heart of various companies like Google, Microsoft, Amazon, LinkedIn, Facebook, IBM & others. They all have embedded it for different applications like product recommendations, displaying search results & so on.
Because of the requirement of the high volume of data, computing and the complexity of machine learning, Python is preferred by the majority of the experts as it is considered the most suitable programming language for meeting all its requirements.
It is mostly because of its extensive or mature sets of statistics & mathematics-based libraries, ease of use, speed, extensibility and huge support from all over the world. This programming language not only combines remarkable power with the clear and simple syntax but comes with classes, modules, exceptions, dynamic typing and high-level data types.
Below are the main reasons why experts and developers prefer Python for machine learning.
1. Easy to learn
One of the biggest advantages of using Python is that it does not require any hardcore programmer to use it. It is very easy to learn and anyone with just the basic knowledge of programming can start programming with Python. It is more of a beginner’s friendly language as a person working on any project based on machine learning can spend more time on his or her project instead of fixing the bugs. Also, it is a very flexible and independent language that can be used for different platforms and technologies. Moreover, this language also gives you the option to choose from a scripting or OOPs approach.
Definitely, its vast libraries are the biggest advantage, but this programming language also comes with extensive documentation and tutorials. When it comes to project or code documentation, the readability is a primary focus for many Python developers. Along with this, its community provides rapid prototyping, quick development, and friendly syntax and so on for developing cognitive systems. Throughout the world, its developers regularly provide comprehensive support and are ready to help you with hundreds of tutorials, forums, etc which makes it easy for you to code for any given project.
With over 2 decades since its first launch, Python has become significantly more versatile. As of now, Python can be implemented on almost every operation, working or managing cloud infrastructure, operations, etc. This versatile programming language supports structured programming, object-oriented programming and functional programming patterns.
Furthermore, it can not only be used for machine learning or data science projects but also used for web frameworks, game development, mobile app development, networking and others. It also serves as a good back-end tool for data handling with Spark support for managing a large amount of data.
4. Framework & libraries
The abundance of open source frameworks and libraries is one of the major factors of Python which attracts developers from all over the world. Among numerous libraries, some of the most prominent and widely used libraries for machine learning are TensorFlow, Matplotlib, Pytorch, SciKit Learn and others.
Not only machine learning, but this programming language also has a good number of libraries for other applications like Django for integrating web applications, pandas for machine learning, SciPy for scientific computing, OpenCV for images, NumPy for text, Librosa for audio and others.
Other notable libraries for artificial intelligence & machine learning are PyML which focuses on SVMs & other kernel methods, PyBrain for simple and effective algorithms for machine learning tasks, pyDatalog acts as a logic programming engine in Python, AIMA for implementation of algorithms using Python, EasyAI for acting as a simple Python engine for 2-players game with AI, etc.
We have already discussed that large organizations like Google, Facebook, Amazon and others are using it. Other than that, this programming language is also known for having a vast community that is known for contributing and helping others. This constant update by the developers’ community support also makes this programming language one of the most effective tools for machine learning applications.
Top Python Libraries for Machine Learning & AI Applications
Machine learning is indeed important and everyone is looking to try their hands on but implementing AI & ML algorithms is not that simple. It requires a well-structured and well-tested environment to empower developers to come up with the best quality coding solutions. For reducing the development time, Python is packed with countless libraries for machine learning.
The libraries or frameworks are pre-written programs that can be used directly for coding tasks. Some of the top Python libraries for machine learning are as follows:
It is one of the most popular libraries in the world for performing high-end computations of machine learning. TensorFlow is an end-to-end Python Ml library that can handle handwritten digit classification, Natural Language Processing (NLP), a deep neural network for image recognition, recurrent neural network, word embedding and Partial Differential Equation (PDE).
This library ensures excellent architecture support which makes computation deployments very easy across different platforms such as mobile devices, servers and desktops. Another crucial benefit of TensorFlow for artificial intelligence and machine learning is its abstraction. It allows anyone to focus more on the app’s comprehensive logic rather than dealing with the mundane details for implementing different algorithms.
Ultimately, the TensorFlow library helps developers from all over the world for leveraging AI & ML effortlessly in order to create unique responsive applications that can even respond to user inputs such as voice expression or facial recognition.
It is another leading open-source library for Python users which is widely used for constructing neural networks and machine learning projects. It can easily run on Microsoft Cognitive Toolkit (CNTK), TensorFlow, Theano, MXNet or Deeplearning4j.
It offers different standalone modules like optimizers, activation functions, cost functions, regularization schemes, initialization schemes and neural layers. With this library, you can even easily add new modules similar to adding new classes and new functions. And you don’t need to have a separate model config files, as the model is already defined in the code.
If you are beginners in the machine learning world, then with Keras you can easily design and develop different neural networks. Moreover, this library also deals with convolutional neural networks and includes algorithms for activation layer, normalization and optimizer. Rather than an end-to-end Python library for machine learning, it functions more as an extensible and a user-friendly interface that enhances modularity and total expressiveness.
It was first released in 2007 and since then; it has successfully attracted a majority of machine learning or artificial intelligence expert and Python developers. At the core, it is one of the most popular scientific computing libraries with which you can optimize, define and evaluate mathematical expressions that deal with multidimensional arrays.
The basics of various AI & ML applications are its repetitive computation of a tricky mathematical expression and Theano helps you to make data-intensive calculation which is almost a hundred times faster than when executing on your CPU alone.
Moreover, Theano is well optimized for GPUs offering extensive code-testing capabilities and symbolic differentiation. Talking about its performance, Theano is a game-changer as it has the potential to deal with computations involved with large neural networks. It also boosts the execution time and development time of machine learning applications, especially in algorithms related to deep learning.
Scikit-learn is another prominent name in the list of top Python libraries for machine learning and artificial intelligence. This library includes a broad range of clustering, classification algorithms and regression. Some examples are the random forest, DBSCAN, vector machines, k-means and gradient boosting. You can also easily incorporate with other scientific and numeric Python-based libraries such as SciPy and NumPy.
You can even use this library commercially, and it supports both supervised and unsupervised machine learning. Some of the top benefits of using Scikit-learn for Python are the reduction of dimensionality, decision boundary learning, outlier detection & rejection, decision tree pruning & induction, feature analysis & selection, unsupervised classification & clustering, advanced probability modeling.
NumPy is also known as Numerical Python, a linear algebra that is developed in Python. The importance of NumPy comes from the fact that majority or almost all other Python-based machine learning packages such as Scikit-learn, ScipPy, Mar-plotlib and others rely on it to a reasonable extent.
It is packed with functions that can deal with complex mathematical operations such as Fourier transformation, linear algebra, random number and features that work with matrices and n-arrays in Python. This library can also perform scientific computations and is widely used for handling images, sound waves and other binary functions.
In various projects related to machine learning, a lot of time is spent by the experts for preparing the data and then analyzing the basic patterns or trends. Here, Pandas comes to the picture. It is another open-source Python library that is known for providing a wide range of tools for data analysis and data manipulation. It also allows you to read data from various sources such as SQL databases, JSON files, CSV and Excel.
With this library, you can manage complex data operation with just a single or a couple of commands. Pandas consist of numerous inbuilt methods for grouping or combining data and filtering time-series functionality. As a whole, this library is not used for just data-related tasks but also serves as the best initial point for creating more powerful and focused data tools.
It has become a popular library in a very short period of time. Basically, it is known as a production-ready Python library for machine learning with an excellent example, applications and use cases that are backed by a strong community.
It also absorbs strong GPU acceleration and you can apply it from applications like NLP. It supports both GPU as well as CPU accelerations providing you with performance optimization and scalable distributed training in both production and research. Its two high-end features are Tensor computation with GPU acceleration and Deep Neural Networks. Furthermore, PyTorch also comes with a machine learning compiler called Glow. It helps in boosting the performance of all deep learning frameworks.
8. Seaborn Python
Finally, I will conclude this list of best libraries for machine learning and artificial intelligence with Seaborn Python. It is an unparalleled library for a visualization based on Matplotlib’s foundations.
Most of us know the importance of data visualization and storytelling for any machine learning projects, as they regularly require the exploratory analysis of datasets for deciding the type of machine learning algorithm for implementation. For this purpose, Seaborn Python offers us an interface based on high-level datasets that makes amazing statistical graphics.
This Python-based library for machine learning helps to easily create different types of plots like violin plots, heat maps and time series. Going beyond the Pandas or matplotlib, this library provides features like performing statistical estimation at the time of combining data, visualizing the suitability of statistical models for strengthening the dataset pattern and plotting.
So these were the top libraries that are widely used by Python developers as per their machine learning projects. They can save a programmer’s time as well as can provide explicit functions that one can build on. By using any of these libraries, you can easily work with high-level analytical functions despite having minimal knowledge of underlying algorithms with which you are working on.
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