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Teach Yourself the Basics of Artificial Intelligence

Teach Yourself the Basics of Artificial Intelligence


Teaching yourself machine learning, the basic building block of artificial intelligence, can seem like a daunting task. With so many different programming languages, resources, and development environments to choose from just getting started can seem overwhelming. Luckily, we have taken the time to sort through many of the machine learning resources available to help you get started. The best part is, every resource listed here is completely free to use, meaning that you can jumpstart your machine learning journey with little to no upfront cost required.

Why is Python the Best Programming Language to Get Started With?

Generally speaking, Python is one of the most versatile programming languages utilized today, as it can be found in the source code of anything from servers to video games. More specifically, Python is especially useful thanks to the numerous libraries available to programmers. These libraries greatly reduce the workload on machine learning developers. Instead of writing lengthy programs, they can utilize a simple library-based command to speed up their workflow. The best part about Python? It’s consistently ranked one of the easiest programming languages to learn by industry experts, meaning you can jump right in and get started, instead of taking weeks to learn a new language.

Where Can I Learn Python and Machine Learning or Artificial Intelligence for Free?

Prior to a few years ago, if you wanted to learn about complex topics like machine learning or artificial intelligence (AI) you likely would have had to enroll in university. Luckily, with the development of online resources such as Coursera, Udacity, and edX you can now teach yourself such topics quickly and from the comfort of your own home. With this in mind, here’s the best option for jumpstarting your career as a machine learning or AI developer with Python:
Python Programming Foundations:

Udacity is one of the leading providers when it comes to technology-specific online courses. This introductory Python course will provide you with all of the tools needed to get started with the language quickly. 

What is the best tool for programming in Python, or another language?

While there are hundreds of different integrated development environments (IDEs) available for developers, it is suggested that beginners start with Atom. Specifically, we suggest their “Atom-IDE” product which is completely free to use and based off of open-source code. Utilizing Atom, you can rapidly prototype applications and update your code. While there are many good options available, Atom is preferred due to its seamless GitHub integration and support for numerous programming languages, including every one mentioned within this article.

If you try out Atom and don’t enjoy using it, Eclipse IDE is another great option. While not as versatile as Atom, it is a good option for those who want to focus solely on Java development. Additionally, language-specific plugins can be added to allow developers to program in languages such as Python within the Eclipse IDE.

Are there any useful languages alternative to Python for Machine Learning?

Yes! There are almost a dozen languages that are currently being widely utilized for machine learning. Think of a programming language as a tool. Just as some people prefer certain tools to complete a project around the house, each developer has their own programming language preference. With this in mind, let’s look at a couple alternative languages that might not be as well known as Python, but are still certainly useful for developers to know:

Java:

Much like how Python is the most popular language for machine learning, Java is currently the most popular language for those wanting to work strictly in AI. It’s a versatile language with dozens of libraries available for developers. 

C#:

Of the languages currently used, C# is the most powerful language for those wanting to work in AI. Oftentimes, this language compliments Java, allowing developers to create powerful multiplatform AI applications that can be deployed to numerous devices

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