Python is a well-regarded, high-level programming language with simple and easy-to-read syntax. Guido van Rossum first released it in 1991, and since then, it has become one of the most conventional programming languages in the world. Python is a general-purpose language with applications for many tasks, including web development, scientific computing, artificial intelligence, machine learning, and more. Python is often termed the best language for machine learning for many reasons.
There are several reasons for taking machine learning with Python. Here are four reasons why Python is the best programming language for machine learning:
1. Simplicity and Consistency
Python is widely used in machine learning due to its simplicity and consistency. Python’s simple and easy-to-read syntax makes it an excellent language for beginners to learn, allowing them to focus on the concepts of machine learning instead of struggling with the underlying code. Python is considered a simple and consistent programming language due to its straightforward and readable syntax. The syntax of Python is designed to be intuitive and easy to understand, making it an excellent choice for beginners. For instance, Python uses indentation to indicate the code’s structure rather than symbols or curly braces. This makes it easy to see the structure of the code at a glance, and it also helps to prevent common coding errors.
Additionally, Python has a simple and consistent object-oriented model. In Python, everything is an object, making it easy to understand how the code is organized and how the different parts of the program interact. This also makes it easy to write reusable code, as objects can be quickly passed around and used in different parts of the program.
An introduction to machine learning with Python is simple and offers consistency in the availability of many built-in functions and modules. These provide a wide range of functionality and make it easy to perform everyday tasks without writing much code. This reduces the complexity of the codebase and makes it more manageable. It also has a consistent naming convention, making the purpose of different functions and variables easy to understand. Most aspiring ML engineers or professionals opt to learn machine learning with Python to make the code more maintainable and reduce the chances of errors.
2. Access to elaborate libraries and frameworks for ML
Libraries and frameworks for machine learning are collections of pre-written code and tools that can be used to build and deploy machine learning models. They provide a wide range of functionality, including data preprocessing, model training, and evaluation. Hence, learning machine learning with Python helps build a strong knowledge of the frameworks and libraries. Some popular libraries and frameworks for machine learning include
- TensorFlow: Developed by Google, TensorFlow is an open-source library that allows you to build and deploy machine learning models. It can be used for many tasks, including natural language processing, building and training a neural network, image and speech recognition, etc.
- LightGBM: LightGBM is a gradient-boosting framework that uses tree-based learning algorithms. It is designed to be more efficient than traditional gradient-boosting libraries, making it well-suited to large-scale machine-learning tasks.
- PyTorch: PyTorch is an open-source machine-learning library developed by Facebook. It is similar to TensorFlow but is more geared toward research and experimentation.
- Theano: Theano is a powerful and efficient library that can perform various mathematical operations, making it a popular choice for machine learning and deep learning practitioners. It is a numerical computation library for Python designed to make it easier to perform mathematical operations, particularly those required for machine learning and deep learning.
- Scikit-learn: It is an efficient and straightforward library for machine learning in Python. It includes various algorithms for classification, regression, clustering, and more.
- XGBoost: XGBoost is a library for gradient-boosting decision trees. It is designed for efficient and scalable gradient boosting and has become one of the most popular libraries for machine learning.
3. Platform independence
Python is considered an independent platform for machine learning because it can be used to build, train, and deploy machine learning models without relying on proprietary software or services. The open-source nature of Python and the wide variety of libraries and frameworks make it easy to implement machine learning models without the need for expensive software or specialized hardware.
Python’s simplicity also means developers can perform machine learning with various skill levels. For example, libraries such as sci-kit-learn provide simple, easy-to-use interfaces for building and training machine learning models. In contrast, more advanced libraries such as TensorFlow and PyTorch offer more flexibility and control for complex projects.
Python also has a large and active community of developers, contributing to developing libraries and frameworks, making them more robust and efficient.
In the introduction to machine learning with Python, students learn fundamental concepts and algorithms of machine learning, including supervised and unsupervised learning, linear and logistic regression, decision trees, and neural networks.
Python is also platform-independent, meaning that machine learning models developed in Python can be deployed on various platforms, including Windows, Mac, and Linux. This allows for maximum flexibility in terms of deployment options and makes it easy to integrate machine-learning models into existing systems and workflows.
4. Wider community
Access to many libraries and frameworks: The Python community has developed various libraries and frameworks for machine learning, such as TensorFlow, Keras, Scikit-learn, PyTorch, and XGBoost, providing a variety of tools and capabilities for building and deploying machine learning models.
Support and resources: The large community of Python developers means that many resources, tutorials, and support are available for those working with machine learning in Python. This makes it easier for developers to learn and work with machine learning in Python, even if they are new to the field.
Constant development and improvement: The community of Python developers is constantly working to improve existing libraries and frameworks and create new ones. As a result, the libraries and frameworks are continuously updated with new features, bug fixes, and performance improvements.
Collaboration: The large community also allows for collaboration and contributions from different people, creating more robust and efficient libraries and frameworks.
Interoperability: The wide community of Python developers has helped to ensure that the libraries and frameworks developed for machine learning in Python are compatible with a wide range of other tools and technologies, which makes it easy to integrate machine learning models into existing systems and workflows.
Conclusion:
Python is the most preferred programming language for machine learning due to its large and active community, many libraries and frameworks, readability and ease of use, and versatility. It is also easy to learn and use, which makes it perfect for beginners. With Python, you can quickly implement complex machine learning models and algorithms, allowing complete focus on machine learning research and experimentation aspects rather than struggling with the underlying code. For anyone embarking on a data science and machine learning course, Python’s supremacy remains resoundingly evident.
