If you're looking to build AI products, or want to be more knowledgeable before diving into the space, you need to learn how to code.
Let's look at some AI coding languages that can help you get started.
Python is the all-around AI coding language. It's easy to pick up, so once you master it you'll have time to fine-tune your AI projects.
There are hundreds of free resources available to start learning if you're a beginner.
You don't need to particular program to run Python. It's available on Windows, macOS, Linux, iOS, or Android. Plus it's open source. Anyone can adapt, update, or add code.
The Python community has tons of frameworks and libraries that you can add to your machine learning or data science projects.
There are tools like TensorFlow, Keras, and PyTorch. With these tools, you can learn to train neural networks, work with large data sets, and more.
C++ is one of the most essential AI coding languages you can learn. It's used to create AI systems that need control over RAM and CPU resources.
C++ is used heavily when creating algorithms and models that rely on speed.
If you wanted to get into the gaming industry, C++ is very popular. It's used to build real-time game engines and graphics libraries.
AI developers use this same tech to build AI applications that use real-time processing, like autonomous vehicles or robotics.
C++ has a steep learning curve, but if you stay consistent with studying, you'll be ahead of the game in building high-end AI systems.
Java is similar to Python in a few ways. It's popular, open-source, and it has a lot of frameworks for machine learning and data science.
But Java is much older, and organizations have used it forever. It has more rules, which makes it difficult to break or misuse code.
Deeplearning4j, Weka, and Java-ML are some of the libraries and frameworks for AI development available in Java.
With these tools, you can create and train neural networks, process data, and work with machine learning algorithms.
Java's syntax makes it one of the more complex AI coding languages. But for large-scale AI infrastructure or machine learning projects, it's a great option.
When you're learning these coding languages, it's best to stay consistent with them. If it's a website, a video, or an app on your phone, commit to daily lessons.
Learning code is like learning a foreign language; it’s meant to be practiced every day.
Also, stick with one learning tool if you can. You can switch platforms if you get stuck but only do that if the resources you have are not enough.
You may want to jump into the next coding language when you finish a course. Instead, build projects based on what you just learned to reinforce your knowledge.