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“It is nearly unanimous in most circles which machine learning course is best for newcomers: Andrew Ng’s Coursera offering,” says KD Nuggets. This is the class released in 2011 that sparked the founding of Coursera, a juggernaut in online education. The course is acclaimed online and has a 4.7 star review over 422 ratings. Wait. It only has 422 ratings? Another highly respected course, in fact, top three in freecodecamp.com review of machine learning courses, Udemy’s Machine Learning A-Z: Hands on Python & R in Data Science, has a 4.5 star rating and over 8119 reviews. That’s 18 times more reviews! Why? Udemy’s course offers more applicable training in the software required in most careers that involve machine learning, Python and R.

Andrew Ng’s course uses Octave. Huh? He explains, “In the past, I’ve tried to teach machine learning using a large variety of different programming languages including C++, Java, Python, NumPy and also Octave…and what I’ve learned after having taught Machine Learning for almost a decade is that you learn much faster if you use Octave as your programming environment.” As recruiters in the field of data science we see the balancing act job seekers have to make between a more formal, traditional education and what employers are clamoring for, applied experience. Udemy comes closer to answering this market need, but over the long term perhaps the foundation laid by Andrew Ng will be more profitable for companies and the individuals who have the more in-depth skill set.

To determine what works best for you visit a Quora post titled, “How Do I Learn Machine Learning?” It has served as a robust resource to education seekers as evidenced by its 468,000 views. It has 93 FAQ answers and makes a number of insightful suggestions on books that will advance your understanding and application of machine learning. Then as you move on to deciding what online course or degree you should pursue, consider the criteria put forth by freecodecamp.com in analyzing online course options:

1. Is there an explanation of machine learning workflow? Does it properly line out the steps to execute the project successfully?
2. How many machine learning techniques and algorithms are considered in detail?
3. Which machine learning and data science tools are taught? Preferably Python, R or Scala.

Gartner’s annual hype cycle report lists five steps new technologies go through: “Innovation Triggers, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment and Plateau of Productivity.” Gartner says machine learning is at the “Peak of Inflated Expectations.” Even though there may be some disillusionment in machine learning’s future, as recruiters in Data Science we see incredible growth and opportunity for candidates with a depth and breadth of knowledge and appropriate software skills in this area.

Contact Smith Hanley Associates Data Science and Analytics Recruiter, Paul Chatlos, at pchatlos@smithhanley.com to learn more about the options and possibilities open for you in this exciting field.