Explore the strengths and weaknesses of Python and Java in the context of machine learning, and determine which programming language is better suited for your needs.
Machine learning (ML) is the part of artificial intelligence (AI) that focuses on how machines “learn” from data through algorithms and models written from code. With the rise in ML and AI popularity across industries, choosing the best programming language for machine learning is critical for success.
Python, well-known for its simplicity and versatility, has emerged as the preferred language for ML. The simplicity of the syntax and the thriving Python ML community support makes it a popular choice for beginners and experienced developers. On the other hand, Java offers a solid foundation for building more robust ML applications thanks to its speed, security, and multithreading capabilities.
If you’re ready to start coding algorithms for ML, look at what Python and Java offer.
What is Python?
Python is a high-level, object-oriented language that also supports functional programming. One reason for its popularity is its built-in data structures, which, combined with dynamic binding and typing, allow for highly efficient coding. Python also supports modules and packages that enable code reuse and system modularity.
But what Python is really known for is its simplicity and readability. It emphasizes clean and concise code, making it a popular first coding language for beginners. And it’s efficient– with Python, you get significant results in fewer lines of code.
Python is a highly versatile language. It was specifically designed to serve a wide range of purposes, and its flexibility allows it to be used across different operating systems, including Windows, macOS, Unix, and Linux.
Python is open-source, meaning it’s available for download, use, and redistribution. It’s released under the GNU General Public License (GPL), which allows users to modify and distribute it freely in Python libraries.
Python has been used in programs like Google Search and the Internet of Things (IoT) and continues to be a first-choice language for coders.
Learn More About Python:
- What is Python Used For? Real-World Python Uses
- Why You Should Learn Python Programming
- Average Python Programmer Salaries by City
What is Java?
Java is a general-purpose language. It’s also object-oriented. Java is a compiled language, meaning your code is directly converted into machine code. This compilation process enables faster execution and gives you more control over hardware aspects like CPU usage.
Java is used all over computing, including in web development, analyzing and storing big data, Android app development, cloud development, and the IoT. Organizations like NASA and Facebook use Java in their everyday system functions.
Part of Java’s popularity comes from its high-level functionality, object-oriented capabilities, and strong security features. It serves as a platform with its own runtime environment called Java runtime environment (JRE) and application program interface (API). Because of this, Java can run programs seamlessly on diverse systems, including macOS, Windows, and various UNIX versions.
The Advantages of Python for Machine Learning
When it comes to ML, Python offers many advantages for engineers and programmers. It’s currently one of the most popular languages for ML.
Ease of Use
As Python is an easy-to-use language, it’s an excellent tool for teams collaborating on ML projects. Python’s simpler models allow faster prototyping and product testing, making ML models easier to deploy in production.
Access to Libraries
Python is open-source, so its popularity has led to a proliferation of accessible ML tutorials that use Python libraries. Python’s attractive features and broad community support make Python an excellent choice for ML.
Easy to Learn
Many newcomers to the world of coding and programming choose Python as their first language. Its simplicity and straightforward language make it easy to learn. Because of this, Python works well as a coding language for new coders interested in ML.
The Advantages of Java for Machine Learning
Python isn’t the only game in town when it comes to ML. Java has its own advantages for engineers interested in coding for ML.
Just-In-Time (JIT) compilers, part of the JRE, are essential to making Java so fast. Java programs run more quickly because it compiles bytecodes into native machine code “just in time” when they’re run. Java compilation does not consume processor or memory resources, helping it stay speedy.
Developers love Java for its security, which is essential to any ML programming. Thanks to the Java Virtual Machine (JVM), memory management, and built-in security features, it’s commonly used for secure programming and applications.
Java is set up so that coders can perform multiple tasks at a time – a process called multithreading. Within the Java Thread program, it’s possible to generate multiple threads and initiate their execution simultaneously. Since ML algorithms often involve complex computations, Java’s impressive multithreading ability can significantly improve the overall performance and speed of ML algorithms.
Python vs. Java for Machine Learning: the Similarities
When it comes to coding for ML, Python and Java have some qualities in common.
Extensive Built-In ML Libraries
Python and Java offer a wide range of libraries and frameworks that support ML-specific development, making it simple for developers to access the information they need no matter which language they choose.
Extensive communities support those ML libraries we just mentioned, thanks to Python and Java both being open-source. This allows developers to get help when they need it – and stay up-to-date in real-time – while coding for ML algorithms.
Python and Java can work together to optimize ML. Python is often used to build ML models, which are then integrated into Java for larger applications. Thanks to this kind of teamwork, both languages can be instrumental in pushing ML forward.
Python vs. Java for Machine Learning: the Differences
Despite the similarities and advantages of both languages, it’s important to note their differences. These may help you decide which language to pick for your ML project.
Python uses much simpler syntax and language. Its clean and efficient code enables it to do more with less. However, coders familiar with Java may find it easier to jump right in with a language they already know.
Prototyping and Scalability
As we said above, Python is great for quick prototyping: do more with fewer lines of code and start running tests. However, Java’s design makes it a better choice for sheer scalability. Both prototyping and scalability needs should be considered when choosing Python vs. Java for ML coding.
While Python and Java have widely available community resources, Python has a much larger community of specifically ML-based support. Java simply isn’t as popular among ML coders and programmers, meaning users won’t find as many tutorials in the libraries and community resources as they would with Python.
Which Language Do I Pick for Machine Learning?
When looking at Python vs. Java for machine learning, you must consider both languages’ strengths and weaknesses – especially the differences between them. Here are a few questions to ask yourself as you make your decision. And remember that Python and Java can work together to create a prototype of your project and then scale it up.
What Am I Trying to Accomplish?
Java is the language of choice for larger and more complex algorithms because of its performance operations, security, and scalability. Yet, Python is more efficient, doing more with less because of its clean, simple syntax.
What’s the Most Important Aspect of This Project?
Java is your answer if you’re looking for speed and the ability to work with large data sets when running your program. However, if you need to assemble a prototype quickly – and one with flexibility – Python is the better choice.
Which Language Do I Already Know?
Python is an incredibly popular programming language and a common first language to learn. This means that coding beginners can start writing ML algorithms and projects almost immediately – and with just a few lines of code. The enormous library and community of Python-based ML resources are there to help get newbies started.
On the other hand, many enterprise-based coders already know Java and might find it easier to start building ML applications with it. While not as commonly used as Python, Java is still an incredibly popular and widely used language that can be translated into ML programming.
Start Developing Your Coding Skills with App Academy
While App Academy’s bootcamps do not directly prepare students for a career in machine learning, we can help you take the important first step of learning Python, Java, and other important technical and soft skills that can help you along your journey. From there, you can work to acquire any additional skills needed along the path toward your dream career.
Schedule a call with an App Academy admissions counselor to learn more about our programs. And, if you’re ready to dig in deeper, start exploring our programs to see which one might be a good fit for you!
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