Let's face it. The hype around big data and machine learning (ML) can be intimidating for noobs. Particularly if you have little background in statistics and/or computer science, you may find this complex territory difficult to navigate. You may wonder how to go about building your toolkit in this space. Where do you even begin?
As a marketing major with no programming background until 3 years ago, I shared these concerns before I decided to chart out my own learning path. In this post, I will share quick and helpful tips that I have found useful for getting started in ML. Specifically, I will share my experience with three online course specializations that can be taken via Coursera.org.
Exposure, exposure, exposure:
What advice what you give to an aspiring swimmer who is afraid to get into the water? Get into the water! Surrounding myself with machine learning conversations and content helped break the ice. A few things to consider:
Structure, structure, structure
Assuming you are now convinced that you want to be more than just an observer to exciting ML developments, it's time to add some structure to your learning. A convenient but effective way to do this is to first find resources that already have some structure. Online courses are my go-to tool. Just like any self-guided learning, they require discipline and dedication. I have been on both sides of the coin -- both an online learner and provider. Two useful tips are to (a) set aside time each week, particularly binge during weekends, and (b) get the Coursera app on your smartphone, at least to stay on top of video lessons.
Finally, here are three sets of online courses that I found most useful and why. Together, when taken in the order below, you can get a lot out of these courses:
Of course, here I have assumed you have all the time in the world to take these courses for a great DIY learning experience. If you are facing a time crunch, I'd recommend the Machine learning specialization from University of Washington. Final tip from a grad student: You can apply for financial aid and save a few hundred dollars via individual course pages. This option is not available if you try to sign up for the entire specialization. Good luck!
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