Why use Python for Artificial Intelligence and Machine Learning
Future technology projects will be hosted with Machine Learning and Artificial Intelligence. The objectives will be to attain better personalization, smarter recommendations, and improved search functionality. Artificial Intelligence has brought in value for enhancing the user experience and creating value across many industries where our apps can see, hear and respond, this is how Artificial Intelligence has become a game-changer.
What makes Python Best Programming Language for Artificial Intelligence and Machine Learning?
AI & ML projects are different from traditional software development where the initial setting of the architecture itself varies. Deep research and a good skill set are the keys to executing a performable AI & ML project. To implement an effective AI & ML project, you should use a programming language that is stable, flexible and has tools available. Python offers all of this, this is the reason why we see lots of Python AI projects today.
Python helps developers be productive and confident about the software they’re building from development to deployment and maintenance. Simplicity and consistency are the factors that make Python the best fit for machine learning and AI-based projects which also include, access to great libraries and frameworks for AI and machine learning (ML), flexibility, platform independence, and a wide community. These are basically considered as the major reason for the popularity of the language.
Known for its simplicity and consistency
What Python as a language offers is concise and readable code. As there are complex algorithms and workflows which stand behind machine learning and AI, Python’s simplicity allows developers to write reliable codebase and functions with ease. Hence Developers get to put all their effort into solving an ML problem instead of focusing on the technical nuances of the language.
Developers are of opinion that Python is more flexible than other programming languages. Others point out the many frameworks, libraries, and extensions that simplify the implementation of different functionalities. It is also a fact that Python is suitable for collaborative implementation when multiple developers are part of a development ecosystem. Since Python is a general-purpose language, it can do a set of complex machine learning tasks and enable you to build prototypes quickly that allows you to test your product for machine learning purposes.
Has a huge selection of libraries and frameworks
Implementing algorithms for Ai & ML is tricky and complex. The importance to have a well-structured and well-tested environment to enable developers to come up with the best coding solutions is vital to develop a strong product.
To reduce development time, programmers turn to a number of Python frameworks and libraries. A software library is a pre-written code that developers use to solve common programming tasks. Python, with its rich technology stack, has an extensive set of libraries for artificial intelligence and machine learning. Here are some of them:
- Keras, TensorFlow, and Scikit-learn for machine learning
- NumPy for high-performance scientific computing and data analysis
- SciPy for advanced computing
- Pandas for general-purpose data analysis
- Seaborn for data visualization
Platform Independence is the key
This term refers to a programming language or framework which allows engineers and developers to implement codebases or solutions on one system and use them on another system without or with minimal changes. One key to Python’s popularity is that it’s a platform-independent language. Python is supported by many platforms including Linux, Windows, and macOS. Python code can be used to create standalone executable programs for most common operating systems, which means that Python software can be easily distributed and used on those operating systems without a Python interpreter.
