How To Break Into Machine Learning in 11 Steps

According to Harvard Business Review, Data Scientist is the sexiest job of the 21st century. With exponential growth in the amount of data generated every day, the world needs specialists who can extract value from that data.

Data science had a tremendous impact on many industries, but machine learning has always been a key driver to digital transformation and automatization.

Machine learning is now everywhere around us: in music, healthcare, social networks, even in chess. The number of applications is huge, and it keeps getting bigger as more and more industries adopt this technology to tackle their problems. The demand for machine learning specialists is constantly growing.

If you’re not convinced about how widespread machine learning is, you can check out these examples of using artificial intelligence at some of the top companies to improve their operations, services and products. You will be pleasantly surprised.

Being part of a rapidly growing artificial intelligence environment is an attractive career path, but how can you enter that path?

You might have already heard that machine learning engineers are expected to be good at math and statistics, know programming languages, have a bit of business sense and solid research skills. It’s all true, but don’t let it overwhelm you.

You don’t have to be proficient in all of the above to start your career as a machine learning engineer. It takes time, effort, and practice to meet all of the qualifications.

How to break into machine learning
  1. Learn essential math skills. …
  2. Study basic computer science skills. …
  3. Earn any necessary degrees. …
  4. Learn a programming language. …
  5. Learn specifics about machine learning. …
  6. Practice with existing datasets. …
  7. Work on your projects and build your portfolio. …
  8. Join a community and attend conferences.

How to Get Started with Machine Learning & AI

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.

How to break into machine learning

Here are 11 steps that can help you begin working in machine learning:

1. Learn essential math skills

Machine learning requires an understanding of several areas of mathematics. If you dont know linear algebra, statistics, probability and multivariable calculus, it may be a good idea to study that material. Although there isnt 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.

2. Study basic computer science skills

If you dont 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 youve learned.

3. 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 youre 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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 dont 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.

10. 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.

11. 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.

Please note that none of the products mentioned in this article are affiliated with Indeed.


How do you break into ML?

My best advice for getting started in machine learning is broken down into a 5-step process:
  1. Step 1: Adjust Mindset. Believe you can practice and apply machine learning. …
  2. Step 2: Pick a Process. Use a systemic process to work through problems. …
  3. Step 3: Pick a Tool. …
  4. Step 4: Practice on Datasets. …
  5. Step 5: Build a Portfolio.

How do I get into the machine learning field?

A bachelor’s degree and a basic understanding of programming concepts and mathematics are the starting point. From there, you will pursue training and certification in machine learning skills, such as the programs offered by Simplilearn.

How hard is it to get into machine learning?

Difficult algorithms: Machine learning algorithms can be difficult to understand, especially for beginners. Each algorithm has different components that you need to learn before you can apply them.

How can I learn machine learning by myself?

Kaggle is a great platform where you can practise your machine learning skills. There are thousands of datasets which you can download and experiment with. Kaggle hosts competitions where you can test your machine learning skills to solve real ML problems.

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