Machine learning engineering: Machine learning engineering is a relatively new field that combines software engineering with data exploration. Though there is no single, established path to becoming a machine learning engineer, there are several steps you can take to better understand the subject and increase your chances of landing a job in the field.
Read on: 21 Best N-Power Training Programmes
Machine learning engineering
1. Get the Idea about machine learning engineering
Machine learning is a branch of computer science involving data analysis, and it can be valuable knowledge for professionals in many different industries. You can break into machine learning through obtaining formal education or even through self-study. If you’re interested in this field, you may want to know more about the steps that can help you learn about, practice and get a job in machine learning. In this article, we discuss steps to help you break into machine learning, even if you have no math or computer science background.
2. What is machine learning?
Machine learning is a field of computer science that involves teaching computers to analyze data. In machine learning, an engineer instructs a computer to collect and interpret data through the use of algorithms. Then, the computer makes data abstractions in order to make predictions based on that data. Data abstraction means reducing data to its basic, or essential, qualities and hiding nonessential details. Machine learning is a type of artificial intelligence.
Machine learning engineering
3. Learn essential math skills
Machine learning requires an understanding of several areas of mathematics. If you don’t know linear algebra, statistics, probability and multivariable calculus, it may be a good idea to study that material. Although there isn’t a strict requirement to learn all of these types of math in depth, they can benefit you as you get into machine learning. You can study this math using online or physical books, videos and articles. You may also consider hiring a tutor or attending virtual or in-person courses.
4. Learn to code using Python or a similar language.
To become a machine learning engineer, you’ll need to know how to read, create, and edit computer code. Python is currently the most popular language for machine learning applications, but a significant amount of engineers use script formats like R, C, C++, Java, and JavaScript instead.
- Try learning multiple languages to make yourself a more appealing job candidate.
5. Work through online data exploration courses.
Before you learn skills specific to machine learning, it’s important to have a solid foundation in data analysis. This includes subjects like statistics, which will help you understand data sets, and feature engineering, which will help you make data-based algorithms. Some high-quality online courses related to these subjects include:
- Intro to Descriptive Statistics from Udacity, which will teach you how to communicate information about data sets.
- Intro to Inferential Statistics from Udacity, which will teach you how to understand and analyze data sets.
- Getting and Cleaning Data from Johns Hopkins University, which will teach you how to obtain and optimize data sets.
- Feature Engineering for Machine Learning from Udemy, which will teach you how to process and manipulate data variables.
Machine learning engineering
6. Study basic computer science skills
If you don’t have any experience in programming, it may be a good idea to learn basic coding skills. As with mathematics, you can either try to teach yourself or attend training programs to learn to code. It may also be a good idea to practice writing your own code instead of only learning the theory. Practicing can help you remember and apply the information you’ve learned.
7. Complete online courses related to machine learning.
Once you know how to code and understand the foundational principles behind data exploration, start digging into the world of machine learning. This includes subjects like creating algorithms, implementing neural networks, and designing machine learning systems. As a starting point, look into online courses like:
- Machine Learning from Stanford, an introductory class focused on breaking down complex concepts related to the field.
- Learning from Data from Caltech, an introductory class focused on mathematical theory and algorithmic application.
- Practical Machine Learning from Johns Hopkins University, a class focused on data prediction.
- Deep Learning Specialization from Coursera, a class focused on creating neural networks.
8. Earn any necessary degrees
Depending on the job you apply for, it may be a requirement to hold a college degree. Not all jobs in machine learning require a degree, and you may be able to prove your skills through alternative routes, like your project portfolio or your performance in competitions. If the job you’re interested in requires a degree, consider a degree in data science or computer engineering, although others in related fields can also be helpful.
You may be able to earn your degree while you begin to learn about machine learning on your own time. For some of these degrees, the coursework and basic machine learning knowledge may overlap.
Machine learning engineering
9. Earn a relevant certification or degree to help you land a job.
In engineering, many people get high-quality jobs without a formal education. However, accreditations will make you a more valuable job candidate and, in some cases, will be the only way to fulfill a company’s job requirements. To boost your chances of landing a machine learning position, work toward things like:
- Online Nanodegrees in computer science, engineering, and machine learning.
- A Certificate in Machine Learning from the University of Washington.
- An Artificial Intelligence Graduate Certificate from Stanford.
- A Certification of Professional Achievement in Data Science from Columbia University.
- A CSCI E-81 Machine Learning and Data Mining certification from Harvard.
- A traditional undergraduate or graduate degree in computer science or engineering.
10. Work on personal machine learning projects.
When you’re first starting out, try examining and recreating basic projects provided by Scikit-learn, Awesome Machine Learning, PredictionIO, and similar resources. Once you have a solid grasp on how machine learning works in practice, try coming up with your own projects that you can share online or list on a resume.
- So you don’t have to spend time collecting data, try using publicly available data sets from places like the UCI Machine Learning Repository and Quandl.
- If you can’t come up with a project idea, look for inspiration on websites like GitHub.
Machine learning engineering
11. Learn a programming language
Programming languages are a means of communicating with computers so that both humans and computers can understand. Like spoken and written languages, programming languages have their own conventions of grammar and syntax. The most commonly used programming language in machine learning is Python. If you want to work in machine learning, many jobs will likely require you to program using Python, although knowledge of other languages like Java, C++ or R may also be helpful.
12. Participate in Kaggle knowledge competitions.
Kaggle is a dataset database that hosts a variety of machine learning challenges. Some of these are official competitions, which offer monetary prizes, and some are free competitions that simply provide experience.
- To start out, try completing the beginner competition Titanic: Machine Learning from Disaster.
13. Learn specifics about machine learning
In machine learning, you typically work with concepts such as deep learning frameworks and algorithm libraries. For example, Scikit-learn is a library of classical machine learning algorithms. It might be helpful for you to study these algorithms, as they are common in machine learning. You can also learn about other data-handling libraries, such as NumPy and SciPy.
Machine learning engineering
14. Practice with existing datasets
There are free datasets available online that you can use to practice using machine learning. Using previously gathered data, you can focus on applying what you have learned without the time-consuming steps of collecting data. You can select data for different qualities of the data with which to practice.
Examples of qualities you can select include the number of instances, which are collections of information at a given point in time, such as medical records. Attributes are another quality you can select, which are descriptors such as dates or ages.
15. Apply for a machine learning internship.
While personal projects and competitions are fun and look great on a resume, they may not teach you the business-specific machine learning skills required by many companies. So you can gain this experience, look for internships or entry-level jobs related to product-focused machine learning.
- Look for relevant internships on websites like Internships.com.
Machine learning engineering
16. Work on your projects and build your portfolio
When you get more comfortable working with existing data, you can begin to collect your own. After you gather your data, you can clean it and use it the same way as the existing datasets you practiced with before. Over time, you can grow a portfolio achievement to show prospective employers or clients to highlight your skills.
17. Look for machine learning jobs online.
You can find current job openings on classified websites like ZipRecruiter, Glassdoor, and Indeed. Though many companies use the position title Machine Learning Engineer, some may use alternate titles like:
- Data Scientist
- AI Engineer
- Big Data Engineer
- Deep Learning Engineer.
18. Write a resume that highlights your machine learning skills.
When creating a resume for a machine learning position, focus on things relevant to the field such as your professional experience and educational accreditations. For any previous jobs, make sure to list specific things you accomplished related to machine learning.
- If you completed any job-relevant personal projects, feel free to list them on your resume using short, sentence-long descriptions. If possible, include a link to the project so the company can see it.
Machine learning engineering
19. Create a personalized cover letter for each position you apply to.
On every cover letter, list your job qualifications, education, and relevant experience. To personalize your letters, include a unique sentence or 2 in each about what you’ll bring to the company you’re applying to.
- Your cover letters should be no more than 3 paragraphs long.
20. Prepare your application
When you find a machine learning job you would like to apply for, you can customize your CV to highlight the skills you have that best fit its requirements. For example, if the job you are applying for requires working knowledge of a specific programming language, you can emphasize times you have used that language. It might be helpful to highlight your practical experience, which could help you distinguish yourself from applicants whose knowledge is primarily theoretical. You can also include information about the most impressive projects in your portfolio, detailing the specifications of each.
21. Submit the job application.
To apply for an engineering position, fill out the official job application provided by the organization in question. Then, submit the application using whatever method they require. Don’t forget to attach your resume, cover letter, and any other requested documents!
- Since machine learning positions are tech-based, expect to fill out most of your applications electronically.
- Before submitting your application, check it thoroughly for any spelling or grammar mistakes.
22. Interview for the job
Machine learning job interviews may include several parts. You may have a standard one-on-one or panel interview, where both you and the interviewer have the opportunity to ask questions. You may need to describe specific technical knowledge to demonstrate your level of understanding. Additionally, you might need to explain how you would approach a particular problem or project.
Another part of the interview could be to demonstrate your technical skills. You may need to produce code using a keyboard or write it down physically. In addition to showing the quality of code that you write, you can also use this as an opportunity to discuss your thought process and how you would address any issues that arose from the code.
Machine learning engineering
23. Create and run machine learning experiments.
As a machine learning engineer, you’ll be tasked with solving specific problems using your employer’s internal data. To do this, you’ll need to come up with and test out various experimental algorithms that yield results relevant to the task at hand.
24. Build and implement machine learning systems.
25. Ensure the data pipelines run smoothly.
Machine learning engineering
26. Participate in educational programs to earn promotions.
Once you’ve established yourself with a company, you may reach a pay ceiling based on your current education level. To gain additional raises and promotions, you may have to get a machine learning certification, earn a degree, or participate in specialty courses.
- Some companies will fund your additional education, though others will require you to pay out of pocket for it.
27. Join a community and attend conferences
You can participate in online message boards, social media groups and chatrooms with other people interested in machine learning. These spaces give you the chance to talk to others from anywhere in the world and share experiences and tips. Professional conferences also allow you to develop your skills, especially by learning about the latest developments in the field. At conferences, you may also have the chance to meet other professionals who can help you with professional networking or whom you can contact when you face a challenge.
28. Develop your communication skills
Even though you are learning to teach computers, you might also work and communicate with people to break into machine learning. For example, there is a good chance that you may interview, whether in person, over the phone or online, to get the job. Once you have the job, you often have to work with a team.
You might need to explain complex concepts to your team members, especially if they don’t have a background in computer science, and listen to their goals and feedback. If an issue arises during your work, you may also need to communicate the cause and explain a solution and anticipated timeline to address the problems.