While the concept of Machine Learning (ML) dates back to decades ago, it has come to the global spotlight only recently with the upsurge of Data Science and its related concepts like Artificial Intelligence, and Artificial Neural Networks. Today, magnates of the business and IT world such as IBM, Google, Facebook, and Amazon are leveraging ML technology in their products and services to foster innovation. This has created a huge demand for skilled and trained ML professionals both in the business and IT sphere. According to LinkedIn research, today, the number of ML engineers have increased by 9.8 times than it was five years ago.
So, folks, this is the time to make it big as a Machine Learning engineer! But before you do, you must first understand what it takes to make a successful ML engineer.
- Develop Coding Skills
Learning to code in a specific programming language is a must for becoming an ML engineer. So, you have to pick one programming language and master it before moving to another programming language. Python is currently one of the most favorite programming languages of developers around the globe. Apart from this, you could also try learning Java, Ruby, C, C++, Perl, and Scala.
- Online Courses
Once you master a programming language, you must develop data analysis skills. You have to learn Statistics, data structures and algorithms. Lucky for you that now there are numerous online platforms offering courses on data analysis and statistics. Here are some courses that you could opt for:
- Intro to Descriptive Statistics (Udacity) – This course focuses on communicating information about datasets.
- Intro to Inferential Statistics (Udacity) – This course focuses on data set analysis.
- Getting and Cleaning Data (Johns Hopkins University) – It specializes in training students on how to optimize data sets.
After data exploration, it’s time to dive into the world of Machine Learning. One of the best ways to learn and master the discipline is to enroll in online courses. There are numerous informative and helpful online courses offered by reputed universities as well as trusted online platforms:
- Feature Engineering for Machine (Udemy)
- Deep Learning Specialization (Coursera)
- Cognizance: Machine Learning Course (Coding Ninjas)
- Machine Learning (Stanford)
- Learning from Data (Caltech)
- Practical Machine Learning (Johns Hopkins University)
- Get A Certification
While formal education and degrees (graduate/postgraduate in CS or engineering) are necessary, getting relevant certifications or accreditations will provide you an edge over your competitors when you apply for that dream ML job. You can boost your chances of getting hired by choosing among these excellent certification programs:
- Artificial Intelligence Graduate Certificate (Stanford).
- Certificate in Machine Learning (University of Washington).
- A Certification of Professional Achievement in Data Science (Columbia University).
- CSCI E-81 Machine Learning and Data Mining certification (Harvard).
- Get Handsy – Work On Machine Learning Projects
Haven’t you heard, ‘Practice makes a man perfect?’ Machine Learning is no exception to this age-old rule. Working on your own ML projects is a great way to hone your skills and understand both your strengths and weaknesses. You can try out tools such as Scikit-learn, Awesome Machine Learning, and PredictionIO, to explore and create basic ML projects. GitHub is a great place to look for ideas for ML projects.
- Take Up Challenges
There are various online platforms that host ML challenges including Kaggle, RAMP, AutoML Challenge, Innoplexus Online Hackathon, and MLflow, to name a few. Make it a point to participate in these challenges and competitions. You will not only learn better but you will also gain experience and become a faster and more focused coder.
- Get Hands-On Training With Machine Learning Internships
Although you can learn a great deal from online courses and ML competitions, they aren’t enough to teach you about the business-specific ML skills. This can only be gained through ML internships where you’ll get to solve real business cases with ML tools. Internships will not only help you get the experience you need to get started in the industry, but they will also allow you to understand what kind of approach you need to take for the real world.
These six steps should be enough to transform you from a rookie to an ML pro, provided you are diligent and dedicated towards it. Learning something new is always challenging, however, just remember – “To begin, begin.”