Become a machine learning engineer on Educative

What is machine learning?#

Artificial intelligence refers to the overarching discipline of creating intelligent machines, such as manufacturing robots, natural language processing (NLP) tools, self- driving cars, and more. Machine learning, a subset of AI, refers to systems and algorithms that can learn independently from particular experiences and data sets.

Machine learning is commonly used with AI technologies, but ML refers specifically to programs and systems that can learn and make decisions on their own. Some of these technologies include medical diagnosis programs, image recognition software, email filtering systems, financial market analysis tools, and much more.

Why are ML and AI so popular?#

These technologies are an important part of the digital world. Companies everywhere are investing in ML engineers to continue the work and solve high-level problems. Let’s take a look at some of the reasons that the market for ML and AI is increasing.

• Big Data boom. Data is the lifeblood of the economy and business. Because of the data boom and IoT, the world is flooded with information, everything from emails, marketing traffic, social networks, financial reports, and more. Companies struggle to keep up with this pool of data, and analyzing big data can’t possibly be done by a human alone. But machine learning provides methods and tools for doing just that. These systems read large amounts of data and describe interactions in actionable ways.

• Matured field. The field of Machine Learning has matured over the past decade with new technologies and a larger community. ML developments have become more integrated with day-to-day industry practices, everything from healthcare to traffic lights to search algorithms. Skills in machine learning and artificial intelligence are now applicable to almost any industry, so the skillsets have become essential to any workforce.

• Integrated tech. Using a smart-phone is now essential for day-to-day human life. Developments like self-tracking Fitbits and virtual personal assistants have become integrated with human life in just a short period of time. Companies everywhere need tools to keep up with a global digital market. Everything from online fraud detection, online customer support, and email filtering all rely on Machine Learning technologies to make the world function at the fast speed we require.

Why should you learn machine learning?#

So, you know why machine learning is important for our world, but why should you become a machine learning engineer? Let’s take a look at how your career can evolve if you learn machine learning.

• Increase your hireability. According to reports from LinkedIn, machine learning engineers rank #1 for growth and market, increasing nearly 10 times since 2012 and surpassing other high demand jobs by a landslide.

  In the past four years, hiring growth for this role has grown by 74% annually. With machine learning skills, you’ll be a desirable candidate at almost any company. These skills are a sure way to stand out and bring great value to the table.

• Get paid. A lot. Machine learning engineers get paid a lot of money. This career averages a yearly salary of $146,000. Even entry-level machine learning engineers average a salary of $97,000, surpassing the average for software engineers by more than $20,000 annually. Because of fierce competition, these rates rise quickly. In fact, senior positions reach an annual salary of $181,000.

• Machine learning won’t go out of fashion. Machine learning is the future. Companies everywhere are investing in machine learning engineers in the long run. These technologies are unlikely to go out of fashion, especially as they become more integrated with day-to-day life. It’s a secure career for today and for the future.

• Work on creative projects. Machine learning engineers get to work on exciting, real-world challenges than engage creativity and analytical skills. As an ML engineer, you’d get to develop creative solutions that have a deep impact on business. A career that deals with real-world problems comes with higher satisfaction and engages more than your coding skills alone.

• Have a lasting impact on the world. Machine learning and AI technologies are used to solve all kinds of humanitarian issues, and many engineers attest to ML’s capacity to create ethical solutions. ML has been applied to all kinds of human struggles to make people’s lives easier and safer. Not every job in tech gets to flout the mission of improving the human experience!

• Open doors to data science and deep learning. Machine learning skills open doors to other career avenues, namely deep learning and data science. ML engineers easily gain valuable expertise in multiple fields, making it possible to side-step into other projects, challenges, and opportunities. You won’t easily get stuck in a box.

How do you learn machine learning?#

So, how does one actually become a machine learning engineer? Turns out, there are a lot of misconceptions about machine learning! No, you don’t need a fancy degree or experience with theoretical work.

In fact, if you’re already an engineer, you’re more than halfway there! Let’s take a look at the skills you’ll need to become an ML engineer.

1. Understand the basics.#

As an engineer, self-taught or not, you need to have the basics of computer science down. This includes the basics of C++, Python, and algorithms.

2. Take an ML course designed to support your learning goals.#

You’ll need to learn the techniques and frameworks required to transition to a data-oriented career. Start with the crucial coding skills and problem-solving intuition, and then apply your learning to real industry challenges and production-level models.

Educative’s Become a Machine Learning Engineer Pathway is a great place to start, guiding you from the basics to real-world case studies. This pathway will also get you familiar with data analytics and data science for machine learning engineering.

3. Advance to courses on necessary ML frameworks.#

There’s a lot of great open-source frameworks and tools you’ll need to know. You should be comfortable with NumPy, scikit-learn, Pandas, and TensorFlow to start.

Most of these frameworks are open-source, so you can leverage community-driven projects created by major tech giants, like Google and Facebook.

You can learn these frameworks in Educative’s Machine Learning track.

4. Find an industry focus you enjoy.#

What interests you? As you study machine learning, you’ll get a sense of which vertical interests you most in the industry. Luckily, with how widespread machine learning is, you’re bound to find a perfect fit.

5. Go deeper.#

If you are interested in research, then studying theory is going to be an important next step.

Always start with hands-on, practical concepts, but open yourself up to theory once you get the basics down. It can be an exciting next step to further your knowledge.

Your first step to becoming a machine learning engineer#

Educative’s Become a Machine Learning Engineer learning path is a perfect first step to jumpstarting your ML career. The five courses in the series by Adaptilab, designed and written by experts in the industry, guide you through

• The basics of machine learning and frameworks
• Machine learning and image recognition
• Natural language processing
• Deep learning
• Real-world practice with case studies

These courses cover everything you’ll need to feel confident applying to machine learning engineer positions.

Continue reading about machine learning#

• How Machine Learning gives you an edge in System Design
• What is Deep Learning? A Tutorial for Beginners
• What is Natural Language Processing? Recent advances in the field