According to statistics compiled by Techjury, people created 1.7 MB of data every second in 2020. As a result, the entire digital universe took up 44 zettabytes by the end of 2020, which has since grown substantially.
Machine learning is useful for analyzing and deriving meaningful insights from all of this data. This data is the framework for a machine learning engineer’s job.
If you want to learn how to become a machine learning engineer and enter this exciting profession, keep reading for ou
r eight-step guide. We’ll also introduce you to the Coding Dojo Data Science Bootcamp, which will equip you with the practical skills to use machine learning algorithms in Python.
But first, let’s define what machine learning is.
What Is Machine Learning?
Machine learning (ML) is a subset of AI that focuses on understanding and building methods that leverage data to optimize the performance of tasks. When applied to software applications, ML algorithms use historical data as input to predict (or infer) new output values.
Machine learning engineers research, design, and build self-running AI systems that automate predictive models.
What Does a Machine Learning Engineer Do?
While similar to the role of data scientists, ML engineers focus on constructing and managing predictive models and AI systems. Data scientists, in contrast, specialize in deriving meaningful insights from large data sets.
Here are the primary job responsibilities of ML engineers:
- Work with the data science team (including data scientists, analysts, engineers, architects, and administrators) and external stakeholders.
- Evaluate, analyze, and organize large amounts of data.
- Execute tests and optimize ML models and algorithms to create accurate predictive models; train and retrain these systems when necessary.
How to Become a Machine Learning Engineer In 8 Steps
1. Learn the Machine Learning Basics
Becoming a Machine Learning Engineer will require extensive knowledge. The best way to understand machine learning is to start with the fundamentals.
If you don’t have that experience in Machine Learning, consider learning the basics first:
- Read machine learning books
- Listen to machine learning podcasts
- Watch machine learning tutorials
2. Enroll in a Machine Learning Bootcamp
Machine learning bootcamps are intensive, short-term programs that equip students with the programming, data science, and machine learning skills they need to succeed as ML engineers.
The Coding Dojo Data Science Bootcamp is an excellent option if you want to learn the fundamentals of data science and machine learning with Python.
3. Learn Essential Programming Languages
To become a Machine Learning Engineer, you’ll need to be familiar with a range of programming languages. Python and C++ are the most widely used programming languages for Machine Learning Engineers.
Here are other programming languages to consider learning for a machine learning career:
4. Complete Machine Learning Projects
Completing your own machine learning projects will help you build a solid understanding of how it works. Working on projects is a great idea to push yourself to learn machine learning, and you can then add these finished projects to your coding portfolio or list them on a resume.
You can start by recreating basic projects like those listed on Awesome Machine Learning, PredictionIO, or Scikit-learn. As for data sets, publicly available data sets are available on Quandl, UCI Machine Learning Repository, and similar resources.
5. Join Online Machine Learning Communities
Joining online machine learning communities will help you connect with the experts and learn from them. A great option for beginners is Kaggle, which hosts data science and ML community members. Check out this resource for other viable online ML communities.
6. Create a Machine Learning Engineer Portfolio
All ML engineers who want to be taken seriously in the job market need to have a machine learning engineer portfolio. Also referred to as coding portfolios, these could be websites where you can showcase your best coding projects. Alternatively, you could use GitHub to house your portfolio.
Check out this GitHub user’s portfolio for a great example of a machine learning portfolio.
7. Build a Machine Learning Engineer Resume
Another key element in your journey to becoming a bona fide ML engineer is to build a robust machine learning resume. Aside from adding your contact details, also add links to your ML engineer portfolio or GitHub profile.
8. Apply for a Machine Learning Internship or Job
While bootcamps and student projects are great for mastering the fundamentals of machine learning, the best way to broaden your skill set, gain experience, and learn the business side of your profession is to apply for an internship or entry-level job. Check out reputable job sites and word-of-mouth referrals to begin the process.
Machine Learning Engineer Roles and Responsibilities
Below are some of the key roles and responsibilities of an ML engineer:
- Work with the data science team and external stakeholders to build and maintain self-running AI systems that automate predictive models.
- Research and deploy machine learning tools and algorithms.
- Run ML tests and use results to improve models.
- Develop ML apps as per clients’ requirements.
10 Skills Needed to Become a Machine Learning Engineer
To become a highly-skilled ML engineer, you’ll need to master certain fundamentals, develop a broad skill set, and gain practical experience.
1. Computer Science Fundamentals
You’ll need to develop a strong understanding of data structures and algorithms (including arrays, multi-dimensional arrays, trees, and queues). You should also be able to write algorithms that can search, sort, and optimize code.
2. Programming Fundamentals
You’ll need to become highly proficient with Python, Scala, Java, and C++, as well as their associated ML libraries. Beginners should also consider learning PyTorch, allowing ML engineers to use standard Python statements and implement deep neural networks.
3. Applying Machine Learning Algorithms and Libraries
Part of your job as an ML engineer is to apply machine learning algorithms and libraries to different codebases. You’ll also be designing and creating the AI algorithms that learn and make predictions.
4. Experience With Data Modeling and Evaluation
You’ll also need practical experience with building machine learning models, training them with data, and evaluating their performance.
5. Knowledge of Neural Networks
Neural networks are a type of AI that teach computers how to process data in a manner inspired by the human brain. This ML process is called deep learning. Deep learning harnesses interconnected nodes or neurons in a layered structure, similar to the human brain.
6. Natural Language Processing
Natural language processing (NLP) is a form of AI that allows computers to understand and interpret human language. NLP gives computers the power to recognize speech, analyze sentiment, and automatically summarize text.
7. Applied Mathematics
Machine learning requires a strong foundation in applied mathematics. Some ML engineers recommend an advanced understanding of applied mathematics to succeed in this field.
8. Experience With Probability and Statistics
Probability is focused on predicting the likelihood of future events, while statistics involves analyzing the frequency of past events. You’ll need both skills to succeed as an ML engineer.
9. Software Engineering and System Design
ML engineers fuse software engineering with data science. They also design the systems that aggregate, process, and analyze data to measure predictive outcomes.
10. Experience With Machine Learning Tools
You’ll need practical experience with machine learning tools like Scikit-learn and PyTorch. Others include TensorFlow, Accord.Net, Rapid Miner, and KNIME.
Types of Machine Learning Jobs
Here are some of the specializations you can focus on as an ML engineer:
- Machine Learning Engineer
- Deep Learning Engineer
- Artificial Intelligence Engineer
- Data Scientist
- Data Engineer
- Software Engineer
- Natural Language Processing Scientist
- Machine Learning Researcher
- Artificial Intelligence Researcher
- Business Intelligence Developer
The Average Machine Learning Engineer Salary
For US-based machine learning engineers, Indeed lists an average annual base salary of $111,435. And according to Salary.com, machine learning engineers in the US earn an average hourly wage of between $54 and $66.
Start Your Machine Learning Career at Coding Dojo
Interested in taking the next step? Enroll in Coding Dojo’s Data Science Bootcamp and arm yourselves with the skills to succeed in this financially rewarding field. Classes are online and only take up 20 hours per week for a duration of 16 weeks.
Machine Learning Career FAQ
Why Learn Machine Learning?
If you love aggregating, processing, and analyzing data, machine learning is a great field for you. ML gives businesses a view of business operational patterns and customer behavior trends. ML is also used to support the development of new products and the optimization of existing products.
Where to Learn Machine Learning?
While you can go to a formal school for machine learning, it’s becoming increasingly common for people to take online bootcamps and training courses to learn the skill.
Many reputable companies offer online courses and bootcamps in machine learning, with Coding Dojo being a leader in the field. Coding Dojo also offers in-person bootcamps for those that learn better in that setting.
How to Learn Machine Learning?
You can learn ML online via bootcamps and online courses. These courses are usually spread out over many months and allow you to learn at your own pace. Many established colleges and universities also offer remote and onsite ML courses.
How Hard Is It to Learn Machine Learning?
If you’re analytical and have a strong foundation in computer programming, statistics, and applied mathematics, machine learning should be relatively easy to master. As with any course of study, your aptitude and commitment to learning will determine if it’s “easy” or “hard.”
How Long Does It Take To Learn Machine Learning?
It takes about six months to complete the average machine learning curriculum. The process could be longer for those without prior programming, statistics, applied mathematics, or data science knowledge.