Whether you realize it or not, you encounter machine learning every day. Every time you fill out a captcha, use Siri, chat with an online customer service rep, or flip through Netflix recommendations, you’re benefitting from machine learning.
Machine Learning Engineers work behind the scenes to create the systems that computers need to operate various software. Interested in becoming a Machine Learning Engineer? First, you’ll need to learn:
- What is machine learning?
- What are the best programming languages for machine learning?
- What does a Machine Learning Engineer do?
What is machine learning?
Machine learning is essentially teaching a computer to make its own predictions. For example, a Machine Learning Engineer might create an algorithm that the computer uses to recognize patterns within data and then decide what the next part of the pattern should be.
Patterns can come in many different settings and can be used for a variety of purposes. Common examples of machine learning include:
- Speech recognition: Any application that utilizes speech recognition uses machine learning to identify the words you’re saying and translate them into text the computer will understand.
- Social media: Probably the most well-known machine learning application, social media platforms generate ads and suggestions based on your likes and interests.
- Virtual assistants: Every time you ask a question or speak to your smart devices, they’re learning your habits and better understanding how to answer you.
- Image recognition: You help computers learn the difference between different images each time you’re asked to click the image that’s right side up to verify your identity.
- Streaming services: Every time you watch an episode of your favorite show or click on a new movie, the system recognizes your activities and uses the patterns created to recommend similar content.
What are the best programming languages for machine learning?
If you’re considering a career in this field, you’re probably wondering which programming language is best for machine learning. While you have many options, here are 7 of the most popular:
1. Python
Python is one of the leading programming languages for its simple syntax and readability. Machine learning algorithms can be complicated, but having flexible and easily read code helps engineers create the best solution for the specific problem they’re working on.
Python supports a variety of frameworks and libraries, which allows for more flexibility and creates endless possibilities for an engineer to work with.
Machine Learning Specialists can choose from Python’s many libraries to tackle whatever problems they have in the best and most direct way possible. These libraries vary from artificial intelligence to natural language processing to deep learning. Some of the most popular Python libraries for machine learning include:
If you’re interested in learning one of the most popular and easy-to-learn programming languages, check out our Python courses.
2. R
The R programming language focuses primarily on numbers and has a wide range of data sampling, model evaluation, and data visualization techniques. It’s a powerful language — especially if you’re dealing with large volumes of statistical data.
A Machine Learning Engineer can use R to understand statistical data so they can apply those principles to vast amounts of data at once. The solutions it provides can help an engineer streamline data so that it’s not overwhelming.
R comes with its own supply of packages for engineers to utilize to get their work done efficiently, such as:
3. Java and 4. JavaScript
Java and JavaScript are some of the most widely used and multipurpose programming languages out there. Most websites are created using these languages, so using them in machine learning makes the integration process much simpler.
Both Java and JavaScript are known to be reliable and have the competency to support heavy data processing. Each language also comes with unique machine learning libraries.
Java machine learning libraries:
JavaScript machine learning libraries
- Math.js
- TensorFlow.js
- OpnCV.js
- Synaptic
To start learning how to use either of these languages, check out the links below:
- Java courses
- JavaScript courses
5. C++
C++ is another popular programming language widely used for performance-critical applications that need memory management and speed at the forefront. These features make it an ideal programming language to use when working in machine learning.
C++ is a competent language that can manipulate algorithms and take on memory management at a very detailed level. Moreover, its speed and efficiency enable it to be used to develop well-coded and fast algorithms.
This top favorite has many machine learning and artificial intelligence libraries, such as:
Ready to get started with C++? Try Learn C++.
6. Shell
Shell can be used to develop algorithms, machine learning models, and applications. It uses mathematical models to collect and prepare data. Shell supplies you with an easy and simple way to process data with its powerful, quick, and text-based interface.
Shell is available to use on all operating systems, including macOS, Windows, and Linux. It also comes with libraries that can be utilized in machine learning. These libraries include:
- Ml-notebook
- Dl-machine
- Docker-prediction
7. Go
Go (Golang) is an open-sourced programming language that was created by Google. This intuitive language is used in a variety of applications and is considered one of the fastest-growing programming languages.
Go is capable of working with large data sets by processing multiple tasks together. It has its own built-in vocabulary and is a system-level programming language.
Go also has features like dynamic typing and garbage collection that make it popular with cloud computing services.
Go was designed to make it easier for more people to learn programming. It’s considered one of the easier languages to learn, so you’ll have no problem breaking into machine learning with libraries like:
What does a Machine Learning Engineer do?
Now that you’re familiar with some popular machine learning languages, let’s take a moment to explore what exactly your job would entail as a Machine Learning Engineer.
Your job will vary depending on the company you work for and the specific projects you’re involved in. In general, Machine Learning Engineers use their programming skills to create the systems computers learn from.
This involves preparing the needed data, cleaning it, and finding the correct model to use it. This allows the computer to provide the resulting suggestions based on the patterns it identified. The program developed by the Machine Learning Engineer will then continue to process data and learn how to better suggest or answer from the data it collects.
The responsibilities of a Machine Learning Engineer may include:
- Maintaining, creating, and streamlining data pipelines
- Keeping precise documentation
- Working to improve processes and systems
Some Machine Learning Engineers also create algorithms that help their companies learn about their users preferences and offer personalized suggestions based on their interests. This technology is popular with entertainment, shopping, news, and travel platforms, so there’s a high demand for Machine Learning Engineers across these industries.
Want to learn how to create these algorithms yourself? Check out our Build a Recommender System skill path to start from scratch; and if you’ve already got some Python skills, try Learn Recommender Systems.
Which programming language will you choose?
If you’re still asking yourself about the best language to choose from, the answer is that it comes down to the nature of your job. Each language is unique and used for a specific task. Many Machine Learning Engineers have several languages in their tech stacks to diversify their skillset.
Testing, experimenting, and experience will help you know how to best approach each problem when creating the system needed for whatever machine learning application you’re designing. Choose a language that best suits your abilities to start your machine learning career. To get started, check out our catalog of programming courses.
Or if you want to streamline your learning, try our Data Scientist: Machine Learning Specialist and Machine Learning/AI Engineer career paths. Each course offers step-by-step guidance on which skills you should learn, and by the end, you’ll have everything you need to start applying to entry-level positions in machine learning.
