As artificial intelligence (AI) and machine learning (ML) reshape how we live and the things we use every day like our cars, homes and mobile devices, the engineers who work with these technologies have quickly grown in demand. With an average base salary of $146,085 and a rocketing 344% growth in job postings, Indeed’s study of jobs experiencing the fastest growth and offering the highest pay for 2019 reveals that machine learning has moved up to the top spot from number four last year.
Machine learning is used in everything from cyber fraud detection and self-driving vehicles to the online recommendation engines that suggest movies and music we might enjoy or clothes we’re likely to buy. As we become more dependent on the data that is essential to enriching our lives and used to help and improve many industries, there now exists is a great opportunity to jump into this rapidly growing field.
Whether you’re an engineer looking to make a jump to ML or in a totally different field, check out these five things to help jumpstart your career.
1. Know your math
Not a fan of numbers? Well, maybe this particular role isn’t quite for you. It’s essential to have an understanding of math if you decide to choose a career in machine learning, so if math is your cup of tea, you’re off to a good start. If you’re just starting out in machine learning, here are some math concepts to start brushing up on.
- Probability: You’ll find yourself relying on concepts from probability theory (AKA the study of uncertainty) for deriving machine learning algorithms. Stanford’s probability review will help you learn more about these concepts.
- Statistics: The algorithms used in machine learning are built on statistical models. If you’re a beginner or want to learn more about statistics, Duke’s Statistics with R Specialization course offers a statistical mastery of data analysis including inference, modeling and Bayesian approaches.
- Linear Algebra : This sub-field of math is part of the foundation of machine learning and includes important concepts that will help you gain a better understanding of what’s going on under the hood of the algorithms used in machine learning. MIT’s Open Courseware will help you get started.
2. Develop your programming skills
As a machine learning engineer, you’ll be working with the fundamentals of analysis and design, and creating dynamic algorithms, so your programming skills will have to be legit. According to Indeed data, here are a few of the top languages employers are looking for from machine learning engineers.
- Python: Python tops the list of languages most in demand by employers. Popular for its efficient data processing and scientific computing, Python has a multitude of specialized machine learning libraries that make it easy to train algorithms using various computing platforms. If you’re ready to develop your Python skills, check out this interactive Free Machine Learning in Python course .
- C++:Popular for its rapid deployment of final models, extensive libraries and rapid prototyping, C++ is also used extensively in machine learning. Udemy’s C++ course is specifically targeted at using the language in machine learning.
- Java: A child born of the 90’s, Java isn’t going anywhere and is still used by many machine learning specialists due to high portability, maintainability and transparent language supported by a wealth of libraries. Java is easy to debug, user-friendly and supports large projects with ease. Udemy’s Java Masterclass has all you need to get started.
- R: Created as a statistical language, R has statistical and data-analysis support, and has a huge repository of statistical models and algorithms that can be used for various tasks. Johns Hopkins R programming course is a good place to learn more about this language.
3. Learn about algorithms
Algorithms, central to decision making, are used to receive and analyze data to predict outcomes. As a machine learning engineer, you’ll find yourself working with a wide variety of algorithms. The three main types of machine learning algorithms you will work with are:
- Supervised learning: Supervised machine learning algorithms involve training a machine with data that has been labeled and tagged with the correct answer. Think of it as a teacher teaching a student: the algorithm constantly predicts results based on training data it receives from the teacher and is corrected each time by their teacher. Over time, the algorithm learns and is able to achieve a higher level of performance.
- Unsupervised learning: Unsupervised learning works by analyzing data without labels and then identifying hidden structures within a dataset. In this case, the computer is learning when there’s no teacher or guidance whatsoever. With time, the algorithm is able to teach new things once it learns patterns in the data.
- Reinforcement learning: Reinforcement learning involves an algorithm learning how to perform a task by attempting to increase the rewards it receives for its actions. These algorithms involve trial and error and are typically used in robotics. For example, a robot would learn over time to avoid collisions after receiving negative feedback when it bumps into objects. The negative or positive response received helps optimize the driving behavior of the robot.
Find out about the basics about algorithms by reading more about the Types of Machine Learning Algorithms You Should Know or taking Udemy’s Machine Learning A-Z course.
4. Choose a framework
Finding the right deep learning framework can boost the range of what businesses can accomplish and gain within their realm due to a framework’s ease of use, parameter optimization, scalability and production deployment effectiveness. There are a variety of frameworks available to solve business challenges and we’ve listed a couple to consider.
- Tensorflow: This Python-friendly open source library developed by Google is widely used in machine learning, and it allows users to write in C++, Java and Swift. Tensorflow does have a bit of a learning curve, but there are a ton of online sources and communities to help you out if you need to find solutions for any issues you run into.
- PyTorch: This framework is a good choice for beginners because it’s easier to learn and fairly intuitive. Developed by Facebook, PyTorch can be used to execute deep learning tasks including speech and image recognition, and is popular for its efficient memory usage and dynamic computational graph.
5. Take a comprehensive online course
Most machine learning engineers have a master’s degree or have taken online certification courses. Thankfully, there are a ton of learning resources available online, including free courses offered by companies that are at the forefront of machine learning technology.
- Google’s machine learning course is free, takes 15 hours to complete and includes 25 lessons, along with over 40 exercises and videos of Google engineers explaining AI and machine learning.
- Amazon’s Machine Learning University features more than 45 hours of courses, with 30 self-paced digital courses, videos and labs that teach students the fundamentals of machine learning. These machine language courses are also used to train Amazon’s in-house engineers to forecast consumption, predict the costs of products and create smart AI. The on-demand digital training courses are available at no cost.
- Bloomberg’s course is designed to make important machine learning skills more accessible to those with a strong math background, including software developers, experimental scientists, engineers and financial professionals. This free 30-session course was initially delivered internally for the company’s software engineers and covers a wide variety of topics in machine learning and statistical modeling.
- Coursera’s offering includes Stanford University’s course on machine learning led by Google Brain founder Andrew Ng, and provides a broad introduction to machine learning, data mining and statistical pattern recognition.
Once you get yourself up to speed and are ready to sink your teeth into some online challenges, head on over to Kaggle and test your knowledge by participating in their contests.
As artificial intelligence continues to impact our day-to-day lives and revolutionize the way we work, there will be a huge demand for jobs in machine learning. Now is the time to jump in on this rapidly growing field that has a great need for talent.
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