How to Get a Job in Machine Learning

Are you worried about artificial intelligence taking your job? You're not alone.

The advancement of AI technology has sparked fear and uncertainty in many industries. But here's the reality:

AI isn't taking away jobs; it's transforming them.

The rise of machine learning presents not just a challenge but an opportunity. By adapting to this shift and acquiring new skills, you can position yourself at the forefront of this revolution.

In this guide, learn how to get a job in machine learning. Design your own personalized ML learning path and discover strategies about how you can present your skills to potential employers.

Table of Contents

Why Start a Machine Learning Career?

Design Your Personalized ML Learning Plan

ML 101: Master the Basics

Learn, Build, Repeat

Show, Don’t Tell: Craft Your Portfolio

5 Strategies for Landing an ML Job

How to Get a Job in Machine Learning with 365 Data Science

FAQs

Why Start a Machine Learning Career?

You don't have to look far to find compelling statistics about the machine learning, data science, or analytics job market. The U.S. Bureau of Labor Statistics states that employment in computer and information technology occupations is projected to grow 23% from 2022 to 2032—much faster than the average for all occupations.

So, is machine learning a good career? Absolutely, given the rapid growth and demand in this field.

With AI bursting onto the scene as the latest and greatest technology, machine learning and data science are getting even more publicity. Companies are increasingly adopting some form of data or AI-based solution in their products and processes—making now the perfect time to learn how to get into machine learning.

Knowledge and experience in machine learning open the doors to various roles, from machine learning engineer and data scientist to artificial intelligence engineer—one of the fastest growing roles in the US, according to a LinkedIn report.

So, what salary can you expect in machine learning? Recent Glassdoor data indicates that the average annual salary for a machine learning engineer in the US is approximately $165,000, based on over 7,000 reported salaries.

In other words, the best time to start a machine learning career is today. This guide will show you how to get a job in machine learning effectively.

Design Your Personalized ML Learning Plan

Starting is always the hardest part. There are so many resources out there—getting lost and overwhelmed is easy. That’s why the best first step is to design a learning plan catered to your skills and machine-learning experience.

There are many learning paths already out there, but they will not work if you don’t tailor them to you. That’s where this article comes in.

In this section, we go over the process of designing your personalized ML learning plan, which will help you meet the machine learning engineer requirements—a crucial step in how to get a job in machine learning.

Step 1: Figure Out Your Learning Style

Our past student, Meghan Verschoor, describes herself as a visual learner—she needs video content to see and grasp new concepts.

She said, "I didn't like some of the platforms that were notebook-driven because I found it hard to stay engaged and motivated where there wasn't a human element to my learning." Check out her full story.

Meghan exemplifies the power of adapting to one's learning style. Had she forced herself to follow a textbook-based tutorial, she might have become bored and frustrated, possibly abandoning her dream of entering data science.

By figuring out your learning style, you'll be a more effective learner and enjoy the process more. This is a crucial step in understanding how to get into machine learning effectively.

Here's a quick summary of the different learning styles:

Step 2: Assess Your Knowledge and Skills

Check out the image above of relevant ML skills, such as programming languages (e.g., Python, R), math (e.g., linear algebra, calculus), data processing tools, and SQL programming.

List your qualifications and those needed to meet the machine learning engineer requirements.

Another way to assess your knowledge and skills is to examine the prerequisites for popular ML courses. This will give you an idea of the foundational knowledge needed.

We will also discuss ML basics later in this guide. Use that as a reference to assess your foundational skills level and identify gaps in your knowledge compared to the course requirements.

From here, you'll know where to focus your learning. Beginners should start with ML basics (covered later in this guide). If you have some math and Python knowledge, consider learning about databases and SQL.

Step 3: Choose the Right Resources for You

Your chosen resources should be based on your learning style and skill level. For example, visual and auditory learners may learn less effectively with textbooks, and reading/writing learners may get frustrated when listening to lectures without notes or documents.

With 365 Data Science, you can learn using various resources, including course notes, video explanations and walkthroughs, and practical hands-on examples—great for the kinesthetic learner.

For further resources to supplement your courses, check out our curated lists of educational resources featuring books, videos, and podcasts tailored for readers and visual and auditory learners.

Step 4: Create a Learning Timeline

Once you've determined how to get into machine learning and which resources to use, you can create a learning timeline with self-imposed deadlines and milestones to stay on track. For example, adding your timeline to a calendar lets you get a real sense of your progress—crucial for maintaining motivation.

The reality is that there’s too much content related to machine learning and data science for anyone to learn it all, much less a beginner. Setting up a calendar lets you gauge how far you’ve come.

Step 5: Track Your Progress

Turning your learning into a habit is a reliable way to stay motivated and ensure consistent progress on your machine learning career path.

Even small steps forward add up over time. If you persist and remain dedicated, you'll eventually achieve your goals.

Remember, every bit of progress counts, so keep pushing forward, and don't give up.

Adopt a method of tracking your time spent learning or cross a day off on the calendar if you’ve spent even five minutes learning that day. James Clear recommends the Seinfeld Strategy for tracking your progress so that you can take small daily actions and not break the chain.

With 365 Data Science, you can build up a streak each time you log onto the platform and make some progress. This and other gamified features are a great way to break down your learning into small steps and daily habits.

Step 6: Get Feedback

Scott Young extensively discusses feedback loops and deliberate practice in his book, Ultralearning. He highlights that feedback helps identify mistakes and areas for improvement—essential for progressing in any subject.

Working on projects offers instant feedback; if a project seems beyond your capabilities, it likely highlights a substantial gap in your knowledge.

Working with and learning from others in ML—both beginners and veterans—is a great way to seek feedback and accelerate your progress. With a 365 Data Science account, you can access forums to discuss ideas or challenges you may be facing and share your machine learning experience.

ML 101: Master the Basics

This section discusses the basic concepts in ML, laying the foundation for the rest of your ML learning. Mastering these basics early on can make learning more advanced algorithms and tools much easier and faster, which is essential for those wondering how to break into machine learning.

Math and Statistics

Machine learning is built on the pillars of mathematics and statistics. You don't need to go too deep into the theory. Still, a working knowledge of these concepts is practical later for understanding how machine learning algorithms work and meeting machine learning engineer job requirements.

Programming in R or Python

Programming languages are essential for implementing machine learning algorithms and handling data effectively. You can’t embark on your machine learning career path without knowledge of at least one.

Python and R are the most popular languages in the ML community.

Here are a few key concepts to begin with:

Data Manipulation and Visualization

Learning data manipulation and visualization is essential for preparing data for machine learning. These skills allow you to clean, transform, and explore data, helping you identify patterns and select the appropriate machine learning algorithm for your problem.

Here are a few basic concepts to understand as you begin exploring machine learning:

Databases and SQL

Learning databases and SQL is an incredibly broad topic. But even a few basic skills will enable you to store and retrieve data effectively—a requirement for machine learning projects.

As a beginner, stick to the SQL concepts and syntax you can immediately use as you learn and work on your projects.

Mastering these basics is fundamental for anyone looking to understand how to get a job in machine learning and build a successful ML career.

Learn, Build, Repeat

We recommend that beginners in machine learning take a project-based learning approach. This method involves learning concepts, applying them through building projects, and repeating the process for each new concept or algorithm you know.

Many experts recommend learning by doing—including those at leading educational platforms like 365 Data Science. Tackling real problems helps you understand the nuances and complexities you might miss by reading a textbook or watching a lecture.

The key is to learn, build, and repeat. Keep pushing your boundaries; you’ll see significant progress in your ML learning. Grasping this process is crucial for how to get a job in machine learning.

But how do you start this process?

Show, Don’t Tell: Craft Your Portfolio

A robust portfolio showcases your ability to apply ML concepts to real-world problems. This is crucial for securing entry-level ML jobs and understanding how to get a job in machine learning.

It serves as tangible proof of your learning progress, skills, and projects—making you stand out when applying for jobs, especially if you have little to no professional experience.

So, how do you build a strong portfolio?

• Select your best work: Choose projects that demonstrate various skills and complexity. Include projects covering machine learning aspects, such as data preprocessing, model building, and evaluation.
• Document your process: For each project, write a detailed description that includes the following.

• Problem aimed to solve
• Dataset used
• Methodologies and algorithms applied
• Results and insights gained
• Challenges faced and resolutions

• Use GitHub: Host your projects on GitHub. This not only makes your work accessible but also shows your familiarity with version control and collaboration tools.
• Share on social media: Post about your projects on LinkedIn, Twitter, and relevant online communities. This helps you build your online presence and attract attention from potential employers and peers.

5 Strategies for Landing an ML Job

Step 1: Get Certified

Obtaining certifications in machine learning can significantly enhance your credibility. And for those wondering how to become a machine learning engineer without a degree, certifications are key.

If you are interested in obtaining a degree, here are some popular online data science degrees which you can complete from anywhere.

Our research into various data-related roles this year, however, revealed that while degrees are still desirable, they’re not the ultimate deciding factor for employers. In many cases, skills are valued more highly than traditional education.

In a competitive job market, certifications from reputable institutions demonstrate your knowledge to potential employers—making you a more attractive candidate. They show that you have gone the extra mile to gain formal recognition of your skills.

The following are three of the most highly recommended certification programs.

Step 2: Fine-Tune Your Portfolio

Customizing your portfolio to the job's industry or domain is essential in learning how to get a job in machine learning. Potential employers are more likely to be impressed by projects directly relevant to their field, as this demonstrates your ability to apply machine learning concepts in a context that matters to them.

For example, for finance-related machine learning jobs, you can showcase projects on credit risk modeling or fraud detection. You could also include projects on customer segmentation, demand forecasting, or recommendation systems for the retail industry.

Take the following steps to fine-tune your portfolio:

1. Identify key industries: Determine which industries you’re most interested in and focus your project work accordingly. Popular industries for ML jobs include healthcare, finance, retail, and technology.
2. Select relevant projects: Choose projects that align with the type of work done in the industry you are targeting. For example, if you’re applying for a job in healthcare, include projects like disease prediction models, patient data analysis, or medical image classification.
3. Showcase industry-specific skills: Highlight the tools, techniques, and methodologies you used that are relevant to the industry. For example, mention your experience with natural language processing (NLP) for analyzing medical records or using convolutional neural networks (CNNs) for image analysis in healthcare.

Step 3: Craft a Standout Resume and Cover Letter

Your resume and cover letter are often your first impression to employers. They need to make a strong, positive impact to move you to the next stage of the hiring process.

A well-crafted resume and cover letter can effectively showcase your skills, projects, and certifications in machine learning requirements, demonstrating that you're ready to take on the role.

Most importantly, tailor your resume and cover letter to every job application. Highlight the skills and experiences that are most relevant to the specific role and industry. Use keywords from the job description to align your resume with the employer's needs.

Always proofread your resume and cover letter before sending it out. Your skills and certifications won't matter if your resume is filled with typos and grammar errors.

With a free account at 365 Data Science, you can access our resume builder, making it easier than ever to craft a professional-looking resume. Simply enter your information into the provided fields and download your ready-to-distribute file.

Here are a few more helpful resources:

• 365 Data Science – The Complete Data Science Resume Guide in 2024
• Glassdoor – Writing a Tech Cover Letter
• Indeed – How to Write a Cover Letter
• Harvard Business Review – How to Make Your Resume Match the Job Description

Step 4: Apply for Entry-level Jobs and Internships

Applying for entry-level machine learning jobs and internships is a great first step in launching your machine learning career.

These positions offer real-world experience, allowing you to apply your skills and knowledge in a professional setting. You can also connect with industry professionals who provide guidance, mentorship, and future job opportunities.

Practical experience from these roles strengthens your resume, making you a more attractive candidate for future job applications.

Consider the following three sources to find such roles.

• Job boards: Use Indeed, LinkedIn, and Glassdoor to find entry-level ML jobs and internships.
• Company websites: Visit the career pages of companies you’re interested in. Many companies post internships and entry-level positions directly on their websites.
• University career services: If you’re a student or recent graduate, take advantage of your university’s career services, which often have internships and entry-level role listings.

Check out our YouTube video for more tips: How to Land a Data Analyst Internship with No Experience

Step 5: Prepare for the Interview

The interview process for machine learning jobs is typically very technical and often involves multiple rounds.

Research common data science and ML interview questions to be prepared for whatever the interviewers throw at you. Here are some excellent places to start:

• Data Science Interview Questions You Need to Know 2024
• Top 10 Machine Learning Interview Questions & Answers 2024
• 30 Data Scientist Interview Questions & Answers 2024

Remember, the interview is as much for you as it is for the company. You also get this opportunity to assess whether the company and position fit your career goals.

You should also prepare a list of questions for the interviewer about the role, team, company culture, or recent projects.

If you're feeling nervous, consider doing mock interviews with friends, mentors, or through online platforms to practice answering questions confidently. You'll be surprised at how a little practice will ease your nerves on the interview day.

How to Get a Job in Machine Learning with 365 Data Science

Starting a career in machine learning is an exciting opportunity with great potential. This guide took you through the steps and strategies you need to follow to get a job in machine learning.

To kickstart your journey, sign up for 365 Data Science. Our comprehensive learning platform caters to various learning styles, offering video content, course notes, and practical hands-on examples to ensure effective learning. You can also learn by working on our projects covering various levels, topics, and industries.

By assessing your current skills, designing a personalized learning plan, and setting a learning timeline, you can master machine learning with 365 Data Science.

Remember, we’re here to support your learning.

What is a machine learning engineer?  Explore this step-by-step guide to understand the role and the skills required to become one in 2024. 

FAQs