Major tech companies are increasingly serious about machine learning. That means you can expect some growth in the machine learning field.
Keep reading, and we'll take a look at some of the most important of those skills.
You don't need a solid handle on all the math, but you do need a good working knowledge of several key areas. At a minimum, your background should include:
- statistics and probabilities
- linear algebra
- discrete mathematics
Linear algebra, in particular, plays a crucial role in machine learning.
2. Computer Science Principles
Machine learning engineers use a wide variety of computer science principles in their work.
Algorithmic thinking, for example, is essential to machine learning. In fact, machine learning depends on a number of algorithms specifically designed for the purpose.
Other computer science essentials include:
An offshoot of these core topics includes machine learning models, such as supervised and unsupervised models.
3. Programming Languages
Different programming languages lend themselves to different tasks. Client-side scripting languages work best for front-end development. In other words, they do best at creating what end users see on a screen.
Machine learning, however, happens primarily on servers. That means the best programming languages for machine learning are server-side scripting languages.
A few of the more common machine learning languages include:
4. Distributed Computing
Machine learning works best when it's done with huge datasets. The more data, the more opportunities for learning.
The pitfall is that the computational requirements make it impractical to run the algorithms on one computer. In most cases, it requires spreading the workload across multiple servers. Some cloud computing services can stand-in for an in-house distributed computing system.
Distributed computing keeps the total time for a run down to a more reasonable level. Without a working knowledge of distributed computing, you'll struggle in machine learning.
5. Data Visualization
While the machines don't need data visualization, you will. If for no other reason, you'll want it for developing reports on the results of your machine learning projects.
Parting thoughts on Skills a Machine Learning Engineer Needs
A machine learning engineer needs a fairly robust cross-section of skills to be successful.
It's important to have a substantial background in math with a particular emphasis on linear algebra. You'll want a solid grounding in computer science principles.
At a minimum, you should develop a working knowledge of server-side scripting languages like Python and C++.
Machine learning typically employs large datasets. That means a comparatively high computational load. Understanding the ins and outs of distributed computing will make your life a lot easier.
Data visualization simplifies developing reports on the results of your machine learning projects.
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