Why is Python considered the top choice for machine learning and artificial intelligence programming?
AI projects are not the same as regular software projects. There are differences in the technology used, the skills needed, and the amount of research required. If you want to create an AI project, you need a programming language that is dependable, adaptable and has useful tools. Python meets all of these requirements, which is why it is a popular choice for AI projects.
Python is a useful tool for developers because it helps them work efficiently from start to finish, including creating, deploying, and maintaining software. It is particularly well-suited for machine learning and AI projects because it is straightforward and consistent, and it provides access to excellent libraries and frameworks for these technologies. Python is also flexible, works on multiple platforms, and has a large community of users, making it a popular choice overall.
The Power of Simplicity and Consistency
Python is a programming language that is easy to read and write. Even though machine learning and AI projects can be complex, Python’s simplicity allows developers to create reliable systems without being bogged down by technical details.
Python is also appealing because it’s straightforward to learn. The code is easy for humans to understand, which makes it easier to create machine learning models. Many programmers find Python to be more intuitive than other languages, and there are many frameworks, libraries, and extensions that make it simple to implement different functionalities.
Because it’s a general-purpose language, Python can handle complex machine learning tasks and is great for collaborative projects with multiple developers. With Python, you can quickly build prototypes and test your product for machine learning purposes.
Rich Library and Framework Selection for AI and Machine Learning
Developing AI and ML algorithms can be complex and time-consuming, so it’s important to have a well-organized and well-tested environment to help developers find the best solutions.
To speed up the development process, programmers use various Python frameworks and libraries. A library is pre-written code that developers can use to solve common programming tasks. Python has a vast array of libraries for artificial intelligence and machine learning, such as Keras, TensorFlow, Scikit-learn, NumPy, SciPy, Pandas, and Seaborn. These libraries provide various features, including high-performance scientific computing, data analysis, and data visualization.
Scikit-learn, for example, offers different classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN, and works well with the Python numerical and scientific libraries NumPy and SciPy. In addition to libraries, there are several Python IDEs that provide a full toolset for testing, debugging, refactoring, and local build automation in a single interface.
By using these solutions, development teams can speed up the process of creating their product. They can use existing libraries to implement necessary features, rather than reinventing the wheel.
Developing Machine Learning and AI without Platform Limitations
Platform independence means that a programming language or framework can be used to develop software on one machine and then used on another machine without needing to make significant changes. Python is a popular language because it is platform independent and can be used on many different operating systems, such as Linux, Windows, and macOS. Python code can be compiled into standalone executable programs for various operating systems, which means that Python software can be easily shared and used on those operating systems without needing a Python interpreter.
Moreover, developers often rely on cloud services like Google or Amazon to meet their computing requirements. However, some companies and data scientists prefer to use their own machines equipped with powerful Graphics Processing Units (GPUs) to train their machine learning models. The fact that Python is platform independent makes this training process more affordable and straightforward.
Large Community and Popularity
According to a survey conducted by Stack Overflow in 2020, Python is one of the top 5 most popular programming languages. This popularity means that you can easily find a development company with the necessary skills to build your AI-based project using Python.
In the Python Developers Survey 2020, it was observed that Python is commonly used for data-analysis, which makes up over 55% of use cases shown in the survey and very close to web development which is 50%.
Studies indicate that the number of Python developers is increasing every year. The popularity of Python is growing quickly, and one reason for this is that more people are using Python for data science and machine learning. This segment of Python users is growing faster than any other.
There are more than 140,000 customized Python software packages available online. These packages are used for scientific purposes and can be installed in Python programs. They help developers analyze large data sets and identify patterns. Python is a reliable programming language and is used by big companies like Google, Pixar, and Spotify.
The Python AI community has grown globally and there are many forums where developers exchange experience and share solutions related to machine learning. If you face a problem, it is likely that someone else has already solved it. You can find advice and guidance from other developers in the Python community. So, you won’t be alone and can find the best solution for your specific needs.
Start your Python Journey
Learning Python is a breeze, with numerous options available to you. You can opt for online courses or attend classroom sessions in person.
Regardless of your prior experience, you can start at the basic or entry level. This course has no prerequisites, making it accessible to people with different backgrounds and skill sets. Anyone, regardless of their level of experience, is welcome to participate.
If you seek instant recognition in Python programming, you can start with the PCEP – Certified Entry Level Python Programmer offered by the Python Institute is one of the top options you should consider. If you are serious about your Python programming skills and opportunities, you can pursue PCAP – Certified Associate in Python Programming and finally PCPP – Certified Professional in Python Programming
AI Programming Languages Beyond Python
Artificial intelligence is an area that is constantly evolving, and there are a few programming languages that are very popular for developing AI and machine learning projects. In this article, we will provide a list of those programming languages that offer a variety of tools and resources to developers who want to create AI-based applications.
R
R is a programming language commonly used for statistical analysis and data manipulation. It has packages like Gmodels, Class, Tm, and RODBC that make it easy for developers to create machine learning projects. R is designed for statisticians and can provide in-depth statistical analysis for various fields, including finance and IoT. It’s also great for creating high-quality charts and graphs, thanks to packages like ggplot2 and Shiny. However, compared to Python and Java, R is not suitable for large-scale data products as it’s slower and less flexible.
Scala
Scala is a useful programming language for working with big data. It provides data scientists with useful tools such as Saddle, Scalalab, and Breeze. Scala can handle large amounts of data well because it has good concurrency support. Since Scala runs on the Java Virtual Machine (JVM), it can work seamlessly with Hadoop, a framework used for processing and storing big data in clusters. Although Scala has fewer machine learning tools compared to Python and R, it is still easy to maintain.
Julia
Julia is a good option if you’re looking to create a solution for tasks requiring high-performance computing and analysis. It has a syntax similar to Python and is specifically designed for numerical computing. Julia is also equipped with support for deep learning through the TensorFlow.jl wrapper and the Mocha framework.
However, Julia doesn’t have as many libraries as Python and doesn’t have a strong community yet, as it is a relatively new language.
Java
Java is a popular language that is easy to maintain, portable, and transparent. It is widely used for natural language processing, search algorithms, and neural networks. With Java, you can build large-scale systems with high performance quickly. However, if you need to perform statistical modeling and visualization, Java might not be the best choice. Although there are some Java packages that support these tasks, they are not as advanced as those available in Python, which has a large and supportive community.
Conclusion
Understanding the fundamentals of programming is necessary for developing AI and effective machine learning systems. This collection of AI and machine learning languages does not contain a domain-specific language like HTML. Instead, these languages can be used for multiple purposes.
Access to libraries and library management are key facilitators to getting started with AI and ML programming languages. Apart from that, choosing an AI and ML programming language based on a language you already know will simplify the learning process. Remember, there is no right or wrong programming language for all AI and machine learning applications. Each has advantages and disadvantages. The task at hand and your resources should guide your decision.
