Machine learning engineers are widely unknown for the better part of the population. It's a strange and unusual term for them.
Basically, a machine learning engineer is a more sophisticated programmer who focuses more on developing machines. Those particular machines can learn and apply knowledge without a specific direction.
This field is more directed into Artificial intelligence and the technology behind it. But how can you become a machine learning engineer?
This article will guide you and describe you the whole process and challenges you'll face if you choose this field.
Definition of a machine learning engineer's career
The main focus of the machine learning is the artificial intelligence. In essence, there are programmers but don't code for applications or websites. Their code goes further to perform a very specific task such as, simplifying things, picking up a box or something similar.
They create such programs that the machine will perform a specific action without having to wait for a direction. A great example of this is the self-driving car.
A self-driving car doesn't wait for you to code or to press a button in order to perform a task. It can find a parking spot, and park itself with no assistance required from you. The opposite would be to park the car yourself.
The machine learning engineers can program a machine in such a way to fit you for a very specific need or interest you might have.
The requirements you need to have in order to become one are:
- Get a master's (or PhD) in the related field
- Have great skills in programming
- Excel in understanding mathematics
- Have great knowledge in clod applications
- Understand in essence computer languages
In order to become an advanced machine learning engineer, you need to follow these steps carefully. It's one of the most complicated careers you can choose from the computer science field.
How to become a machine learning engineer
To break down our topic further, we've concluded the 5 main steps you need to follow in order to become a machine learning engineer.
To become a machine learning engineer you need to:
- Learn how to code
- Complete courses related to the field
- Get certified
- Work on personal projects
- Apply for an internship
Learning how to code
The key to becoming a great machine learning is knowing how to code. Also probably the hardest part.
The most famous languages you can start learning are:
- Python – Python is dubbed as the best out of all the AI development languages
- R – R is the most efficient on the other hand. It has a great environment for analysis and manipulation of data.
- Java – Since AI has a lot of algorithms, Java fits very good in the field
- Lisp – One of the oldest and most suited languages for AI. It's known for the excellent prototyping capabilities.
- Prolog – Close to Lisp when we talk about development. Providing an efficient pattern matching and tree-based data structures.
Math, data statistics and programming are the backbone of the machine learning.
While programming is a vital part of the work, you need to know how to use statistics and linear algebra.
Complete courses relevant to the field
One of the best ways to learn how the machine learning process goes is to take courses.
It's important to have a foundation of what's what in the field, and the courses will help you a lot here. This involves topics like statistics which will help you with the data a lot.
You will also learn data-based algorithms which will be helpful in the future.
Some great online courses you can take are:
- Intro to Descriptive Statistics (Udacity) – This will teach you a lot on how to communicate the information and different data sets.
- Introduction to Inferential Statistics (Udacity) – This will cover the part of understanding and analyzing the data sets.
- Getting and Cleaning Data (Johns Hopkins University) – Involves teaching you how to obtain and also optimize different data sets.
- Feature Engineering for Machine Leraning (Udemy) – This covers the teaching of processing and manipulating with data variables
Getting certified is, even though optional, a great boost to getting a better job. Accreditations will fill your resume and make you look more professional.
You will be more a better candidate for the job, and in some cases it is a requirement from the company offering the job.
In order to get certified in machine learning, you can start by working towards:
- Online Nanodegrees in CS, engineering and machinel earning
- An artificial intelligence graduation certificate -Stanford
- CSCI E-81 Machine Learning and data mining – Harvard
- Certification of Personal Achievement in Data science – Columbia University
Work on personal projects
Studies have shown that the best way to test yourself is to start your own project. This too applies in the machine learning field.
While you're new at the field, try to examine an re-create basic projects you can find from:
- Awesome Machine Learning
- Similar resources
Once you know how the process goes, and you know how to recreate the basic ones you need to start a project on your own. You will most likely fail, but that's a part of learning
Another upside of it is that you can write it down on your resume and look more professional.
If you're worried about getting data, you can always get data sets from different resources. They're usually free and very easy to use and download.
And, if you're stuck without an idea of what you're going to do than you can always check on repositories from GitHub or Bitbucket.
Apply for an internship
Even though personal projects and competition seems fun, you need more challenges in your career.
In order to become a great machine learning engineer, you need to start by taking an internship first. This help your part on business-specific machine learning skills which you can't obtain elsewhere.
Once you know enough about machine learning, the next big step is to take an internship. You will develop the habit of working specifically with tasks of your field.
No course will teach you every thing, so as long as you don't work for a company or a team you're never going to be a great machine learning engineer.
This way, you can gain experience in the field by interacting and learning with other machine learning experts. You can start by looking in your local area about opening positions in internship, or at websites like Internships.com
Alicia leads content strategy for LearnWorthy managing a team of content producers, strategists, and copywriters. She creatively oversees content programs, awareness campaigns, research reports, and other integrated marketing projects.