This article lists some of the primary reasons that made Python an indispensable part of machine learning and data science.
The Machine Learning – Python partnership has cemented its place in the IT and data science industry. A number of market leaders are implementing Python for a range of tasks including consumer segmentation, web development, process automation, software application development, and many others. According to StackOverflow, Python’s popularity is projected to grow significantly in the coming years. The article explores the importance of Python for machine learning.
To learn more about Python, read our blog on – What is Python?
Here are the main reasons why python is used for machine learning–
The Rich Ecosystem of Python Libraries
Python is a high-level scripting language. It has a growing ecosystem of libraries, frameworks, and tools. These tools and libraries are equipped with pre-written codes, that help users to perform a myriad of functions while saving an adequate amount of time spent in code generation.
Some common Python libraries used for machine learning are –
Matplotlib – Fast processing and high-quality graphics generation, used in data visualization and graphical plotting
NumPy – Creation of a universal data structure helpful in analysis and exchange of algorithms; advanced mathematical operations on huge data sets
Pandas – Data manipulation, data analysis, data alignment, data set restructuring, and segmentation
Scikit-Learn – Data analysis, data mining, statistical modeling
TensorFlow – Build and train neural networks; Pattern detection; Numerical computing
These Python libraries make the implementation of AI and ML algorithms very easy. This helps faster product development as it enables the developer to solve complex problems without rewriting codes.
No one likes to work on complicated things and Python addresses the same challenge. It is a flexible and super-efficient programming language and can be used along with other languages. It offers the developers an option to choose between OOP or Scripting. In addition, it does not require the recompilation of the source code, which makes it easier to see the results. Thanks to its ease of operations, developers can enjoy working in a safe environment with a reduced probability of errors.
Python offers the developers the option to choose the programming styles (discussed below) or even combine them.
Imperative programming– Imperative programming describes which commands a computer should perform. This style helps to define the computational sequences as per the change in the program state.
Functional programming – Also known as declarative programming as it declares the operations to be performed as a mathematical equation, irrespective of the program state.
Object-oriented programming – It is based on the concepts of class and object. Here similar objects form classes. Though not fully supported by Python, object-oriented programming is still very helpful for developers to an extent.
Procedural programming – Procedural programming involves taking up tasks in a stepwise format. Used in sequencing, iteration, modularization, and selection.
Check out the most commonly asked Python Interview Questions and Answers
Simplicity and Consistency
Python offers readable and concise codes. Since machine learning and artificial intelligence involve complex algorithms, the simplicity of Python adds value and enables the creation of reliable systems. This helps the developer to remain focused on the machine learning problem without worrying about the technical details of the language.
A big reason that adds to the success of Python is its simplicity to learn. It is easier to understand and this helps to easily create machine learning models. Another feature of Python is that it is intuitive and is perfect for a collaborative deployment. It allows faster prototyping and product testing as it is a general-purpose language.
Popular Python Course Providers:
|Top Python Courses by Udemy||Popular Python Courses by Coursera|
|Top Python Courses by Udacity||Popular Python Courses by PluralSight|
Python is a binary platform-independent programming language, which means it can run on a range of platforms and software architectures. The developer can write the code, compile, and run on multiple platforms.
Python is popular for its high versatility and can run on any platform, be it Windows, Macintosh, Linux, Solaris MacOS, Unix, and more. Integrating Python with other languages like Java, .NET, C/C++, Perl, PHP, R, etc. is easy.
Also Read – What is Data Science?
Great Community Support
Being an open-source language, Python has strong community support. Python is free, has a number of useful libraries and tools to offer, and its documentation can be accessed online. Programmers can discuss their problem statements in forums and chat with others to find solutions.
In addition, Python has the advantage of great corporate support, with top companies like Google, Facebook, Instagram, Netflix, Quora, etc., using Python extensively. It is interesting to know that Google has created many Python libraries for machine learning such as Keras, TensorFlow, etc.
Check out the best Python Courses online
Good Data Visualization
In data science and machine learning, the presentation of data is very important. To present the data in human-readable format, Python has proved to be very helpful. Python libraries like MatplotIib come with great data visualization tools that help to set up the data, parameters, figures, and plotting very conveniently. Such libraries help to present data in various forms including line plots, histograms, contouring and pseudocolor, images, three-dimensional plotting, paths, and multiple subplots.
Source – Medium
Python has created an emphatic place for itself as the industry has focused on building for the future where data plays a central role, and Python, with its prowess, has become the #1 language of choice for everyone. AI and ML professionals. With this increase in adoption, practical professionals must learn if they aspire to do well in the world of artificial intelligence and machine learning in the years to come.
The practical implementation of Python in machine learning projects and tasks has made the work easier for developers, data scientists, and machine learning engineers. Python can be easily used to analyze and compose available data, which also makes it one of the most popular languages in data science. The rich native expansion also strengthens the advantages of Python, which is more suitable for machine learning, data accounting, etc.
Have you tried the benefits of Python for machine learning in your AI initiative?
If you have recently completed a professional course/certification, click here to submit a review.
Download this article as PDF to read offlineDownload as PDF