The Courses and Sources to Get You There
In a Glassdoor poll, data scientist was the top job choice among readers; yet there’s currently a scarcity of data scientists to fill the need for the development and management of machine learning and other AI technologies.
Experts in machine learning make data actionable by programming correlations into unstructured data to create logical outputs and insights.
Most machine learning applications are coded in languages, such as R or Python, but in addition to understanding these languages, it’s important to understand how data informs business decisions. Because of this, those pursuing a career in machine learning must equally understand the business objectives, along with the technical development of machine learning applications.
In the past couple of months, I’ve been asked by partners, clients and colleagues to provide guidance on how to start a career in machine learning. Fifty percent of the time, the conversation ends in five minutes, but the other times there is a real interest and the conversation continues. In virtue of the brave ones who want to take that long and rewarding road, I’m sharing some of my answers to the question – how do I steer my career to the machine learning and AI path?
Part of my responsibilities as Innovation Director here at Wovenware is to guide the career path roadmaps for our technical team and make sure we are always ahead of the curve, supporting the continuous growth and demand of our AI practice. There are many blogs, courses and answers on Quora and other sites on growing careers in machine learning that you can research on your own, but I will share with you the Wovenware way.
A direct route into a machine learning career starts with a science or business administration degree at the university level followed by advanced studies in programming for data science.
The Required Course of Studies
So how do you get the advanced skills? There are many specializations in the AI and machine learning fields, and along with them come specific prerequisite studies, such as:
Math and Statistics. This includes courses, such as numerical analysis & forecasting, linear algebra, multivariate calculus, probability, regressions, and central limit theorem. Khan Academy has excellent courses on these subjects and can provide deep immersion into these studies.
Programming. The preferred programming language for machine learning and data science is Python, and some of the most popular libraries include, Pandas, Numpy, Matplotlib and Scikitlearn. R continues to be the preferred language for statistics and exploratory data analysis, and some of the popular Comprehensive R Archive Network (CRAN) packages, are Caret, RandomForest and e1071. Meanwhile, SQL is still a very relevant language, as relational databases are a big part of the enterprise. Data scientists must be fluent in these languages, while continuing to stay up-to-speed on new frameworks that continue to evolve.
Hitting the Books for Machine Learning Specialization
In order to gain expertise in specific segments of machine learning, it’s important to take specialization courses. A popular and growing specialization right now is Deep Learning Specialization by Andrew Ng, one of the most influential and reputable experts in the field. The course is offered on Coursera a learning platform that he co-founded.
A more general course in machine learning is Stanford’s ML class also by Dr. Ng. This one is more general but very insightful.
Machine learning offers many specialized areas and it’s important to select the field that best fits with your skills and interests. For instance, if you are interested in creating models for image processing, natural language processing and speech recognition, deep learning is probably the route to take. On the other hand, if you are looking to make predictive models to identify churn and customer tendencies, expertise in regression toolkits could be best for you.
As machine learning and other AI applications continue to take off this year, how are other computer scientists and software engineers honing their skills to meet the growing need? I would love to hear your insights and experiences.
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