light blue background showing variours data-related tools like gears, lightbulbs, a brain that turns into nodes, and graphs A recent report from Forbes showed that Machine Learning (ML) Engineering jobs outpaced all others in salary, demand, and growth. That same Indeed report also showed that the need for machine learning engineers has risen by a staggering 344%, with an average base salary of $146,085 (or around $135,825 if you're located in Canada).

A tool to achieve artificial intelligence, machine learning uses algorithms to memorize patterns and apply each new understanding to make better and better decisions. Essentially, machines are trained to think, make decisions, and act on those decisions as the human brain would. The more "training" the machine does, the more accurate it is.

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You already interact with machine learning, likely on a daily basis. From the suggestions Netflix and Spotify give you to proving you're not a robot by selecting a set of images, machine learning has become such a normal part of our lives that we don't even stop to think about how mind-blowing this technology would be just 20 or 30 years ago.

Machine learning is also a rapidly growing field - with most companies lining up to benefit from those with this skill, machine learning professionals will have their pick of the crop, so to speak, when it comes to which industry they'd like to work in. It also goes without saying that the salary for machine learning pros is set to take off in the coming years.

The best part is you can start from scratch and work your way into the data sphere if you choose your path wisely.

Getting your foot in the machine learning door

So we have your attention (or you're just reading out of pure curiosity). Either way, you're probably wondering how to start in this lucrative area. First, you'll have to build a solid foundation of coding, math, and basic machine learning concepts and data tools like Python, SQL, NumPy, and Pandas. From there, you'll need to build your portfolio by working on projects, getting internships, and getting an entry-level data job.

Can beginners learn machine learning?

The best news is that anyone can learn machine learning - even beginners with the right game plan, tools, and a dash or more of determination. In short, to get you where you need to be, here are the steps to learning machine learning (there are more details below):

  1. Learn the basics. Start with essential coding languages like Python (a biggie) and math and algebra.

  2. Learn the theory behind machine learning. Data wrangling, processing, interpreting and presenting models, etc.

  3. Work on open-source projects. Open-source projects are relatively easy to find. If you need a good starting point, check out this article. It's essential to begin developing skills to develop your capabilities and build that portfolio. You'll need to show potential employers you have gained some experience.

  4. Work with various machine learning tools. Scikit-learn, TensorFlow, KNIME, and BigML are just a few you can get started with. For more tools, check out this list. These tools often have models you can build on.

  5. Land that first job or internship. Any experience is better than no experience. Even if that first job is temporary, you are officially a member of one of the fastest-growing careers out there. Congrats!

The necessary training can be done in a variety of ways.

Can I teach myself machine learning?

Getting familiar with machine learning on your own is definitely possible. As stated above, you'll need to grasp the essential topics well. You'll have to love or learn to love statistics and probability. Machine learning algorithms are built on the foundation of linear algebra, not to mention other, more complicated calculus functions.

We also can't forget learning fundamental computer science concepts like computer architecture, data structures, searching and sorting algorithms, and how to compute the complexity of algorithms.

Is Python necessary for machine learning?

Yes, absolutely. Python is necessary to have nailed down if a machine learning engineer is your end goal. The good news is it's one of the most popular programming languages out there, meaning there are loads of resources available to get started. With the right books and tools, you can get started in just a matter of months.

Next, you'll need to teach yourself data wrangling, linear regression, data processing, and interpretation. You can put your skills to the test, helping with open-source projects and participating in pair programming. The work you contribute should be high-quality. Choose an area you're interested in (i.e. don't help out a group working on a project for an airline when your heart lies with healthcare).

This will also take quite a bit of time. You'll likely have to start with more minor, straightforward projects before moving on to those more complex ones. Finally, you'll need to deepen your knowledge, covering everything from data scaling, baseline models, and algorithm test harness to logistic regression, KNN, and bootstrap aggregation, before sending out those job applications and landing your first role.

Pros: cost-efficient and can be done at your own pace. Cons: it can be overwhelming due to the sheer amount of learning. No diploma or accreditation is awarded. You're on your own when it comes to passing certain necessary certifications. Problem-solving can be more challenging as there are no direct teachers or mentors.


Most universities offer some sort of data/data science degree. However, most programs are a master's degree meaning four years of study or relevant work experience is required before applying. Data-adjacent programs like math or statistics can also get you into the data field.

Pros: universities usually offer some sort of practical training, like internships during the summer months. However, this varies from school to school. Good for high school grads or first-timers in the job market. You can also gain the necessary computer science and math basics in addition to coding languages. Cons: lengthy and time-consuming if you're looking to make a quick career change.


You saw it coming. Taking a bootcamp can fast-track the learning process (depending on where you're at on your machine learning journey). Bootcamps are usually done in a matter of weeks or months and are designed to teach you everything you need to know to get you started in the sector.

Our industry-driven data science curriculum gives you the foundation to jump into the data arena. In addition to covering all essential coding languages, you'll dive into sklearn, Spacy, NLTK, Gensim, Tensorflow, and Keras, to familiarize yourself with machine learning basics. You'll also build those oh-so-necessary math skills like linear algebra and getting to know NumPy. You'll get set up with data visualization tools, like Plotly and GeoPandas, as well as learn the development process from design, experimentation (Prototyping), and production code through to deployment.

You'll also finish the program with a portfolio of fully functioning data projects and have access to on-demand, 1:1 mentorship for when you get stuck or need some career advice.

Pros: start your career in a matter of weeks or months. Great for those looking to make a change. Personalized learning with smaller class sizes and regular feedback. Cons: very intense and time-consuming during the bootcamp.

Ready to kickstart your machine learning career? Sign up for our Data Science Bootcamp today and learn from the team that's achieved a 90% employment rate for job-seeking graduates.

At Lighthouse Labs, we believe that 80% of the learning should be and is learned on the job. Experience truly is the best teacher (but our instructors are up there too). Becoming a machine learning engineer or similar will take some time as you generally need to work up to learning the right technical skills and languages (no matter where you are in your career). In other words, it may take more than just an initial education. In the meantime, however, know more about what you'll face when you land that machine learning job.

There are four basics of machine learning.

Supervised learning

Here, machines are taught to mimic the human brain as closely as possible using labelled data sets. The machine takes these datasets and makes the proper adjustments. The process is supervised by the programmers or a team of programmers who ensure the data inputs lead to the correct outputs. Test datasets are updated and added in a continuous loop to check whether the analysis is accurate. This type of machine learning is used in fraud detection and risk assessment.

For example, if we wanted to teach a machine to recognize different breeds of dogs, we would feed it datasets containing information about each dog's fur type, length, colour, size, personality, etc. Eventually, the machine could correctly identify a large dog with typically long, brown fur and a more protective and picky nature as an Australian Shepherd. The process is more complex than that, but you now have a general idea of how it works.

We can divide surprised learning into two categories.

Classification We turn to classification if we only have two possible outcomes (aka binary) or a categorized outcome. Think, Yes or No, Green or Red, Apple or Android. These categories are already found in the input dataset. This type of learning is used in spam detection.

Regression Suppose there is a linear relationship between the input and the output. In that case, linear regression comes into play to solve known issues. Its primary use is to predict the weather, market trends, and even sports forecasting.

Unsupervised learning

No surprise here - unsupervised learning is the opposite of supervised learning, wherein the machine is trained using unlabelled datasets. The machines can then predict the result or output without human intervention. Unsupervised learning can be classified into two main categories:

Clustering Machines place the data into "buckets" based on similarities, differences, and other aspects depending on the desired outcome. Clustering is often used to predict customers' purchasing patterns.

Association Here, machines find relationships between variables within large datasets. How are they dependent? How do these dependencies connect? This algorithm is popular in doctoral work.

Semi-supervised learning

Semi-supervised learning makes the best of both worlds, that is, both supervised and unsupervised learning. When training the machine, both labelled and unlabelled datasets are used. An advantage of this method is that it makes use of all possible datasets, not to mention it's cost-effective. Firstly, using an unsupervised learning algorithm, data is bucketed into groups.

This is familiar to you if you ever took any music lessons. You would practise at home, but when you saw your teacher, they would go over what you learned and guide you through supervised practice until you got it right.

Reinforcement learning

With reinforcement learning, we throw labelled data out the window. Instead, machines learn from experiences (don't we all/what did we say earlier?). The machine learns from feedback. The algorithm searches the data, takes notes of certain features, and learns from prior positive and negative experiences, improving its performance. The machine is rewarded if the output is correct, like when you give a dog a treat because it sits on command.

In the real world, this type of learning is found in building robots, video games, and learning and scheduling tools.

Machine learning careers

So you've got the practical training, the skills perfected for nailing interviews, and the theory taking up sizable space in your machine learning-tuned brain, but what does machine learning look like on the job?

Machine Learning Engineer

As mentioned above, machine learning engineering is a highly lucrative career once you've nailed down the right skills. As a machine learning engineer, you'll be using your coding, computer science, and may knowledge to build to develop models.

Average annual salary: $135,825

Data Engineer

Another path you can follow in the machine learning realm is data engineering. Here, you'll develop and keep up with the data platforms on which AI systems are based. You'll be creating systems for data acquisition, data mining, data process development, and more.

Average annual salary: $115,440

Natural Language Processing Scientist

If you happen to love both language and machine learning (you may be the type of person to have a DuoLingo account), you may be a good fit for an NLP scientist. In this role, you would develop software that learns human speech patterns and then translates them into other languages. You'll need to know at least one spoken language (if you're reading this, you're good) and, of course, machine learning principles.

Average annual salary: $90,645 with an average additional cash compensation of around $9,661 (can go as high as $26,133)

Machine Learning Cloud Architect

As a kid, I was always told I had my head in the clouds. For the Machine Learning Architect, it's roughly the same, although you'll be in charge of maintaining a company's cloud infrastructure. You'll have to build on your machine learning skills and gain knowledge in AWS and Azure, as well as configuration management systems like Chef/Puppet/Ansible.

Average annual salary: $95,247 with an average additional cash compensation of around $11,371 (can go as high as $39,246)

Machine Learning Consultant

A great way to really nail down your machine learning know-how, machine learning consultants help businesses automate their systems, streamline their processes, and save them money using machine learning tools and algorithms.

Average annual salary: $94,997 with an average additional cash compensation of around $11,371 (can go as high as $39,246)

We can't ignore that these roles also lead to upward advancement into more senior positions. For machine learning engineers alone, the salary can climb as high as $200,000 annually.

Get started with machine learning

Taking on the task of learning machine learning is a challenging but accomplishable challenge. While you can teach yourself machine learning, it may be best to eventually take a course just to ensure you're on the right track. If you're ready to jump in, taking a bootcamp like Lighthouse Labs' Data Science course can launch you into the data stratosphere. Both data science and machine learning are fields where you'll keep growing in skills and knowledge. Working your way from data to a career in machine learning may take some time. It would be best if you were sure you're staying on top of the correct certifications to keep you moving up the ladder.

However, breaking into the machine learning world as an engineer, architect, consultant, or scientist means you can officially consider yourself one of the lucky ones; you can call yourself a machine learning professional!

In case you missed it earlier, you can launch your machine learning career with our industry-driven Data Science Bootcamp in just a few months. With 1:1 mentorship, knowledgeable professors, and a small, tight-knight class size, you’ll receive all the tools and help you need to start your data science or machine career strong.