In the last update on my machine learning journey, I had just finished the Udacity’s intro and started with the Coursera / Stanford Intro to Machine Learning. I am happy to say that this course is now complete as well!
It feels slightly surreal to reach this point. When I first setup my plan for ML, Coursera’s course was something I had marked as being challenging and a “maybe if time allowed.” The reviews and the feedback mentioned how great the course was, but also many people seemed to drop off at the neural network chapters. Essentially I had my doubts about being able to finish it on time while doing it part time. There is no time limit to the course, and you could transfer to the next cohort, but I wanted to make sure I did it in the same session I had started. Once you start delaying an online course, there is a chance you will delay it indefinitely.
In retrospect, the course was indeed challenging but not as bad as I expected it to be. The hardest part was to get comfortable with Octave environment and translating lecture notes and formulas into matrix equivalents. I am quite happy that I stuck to the end, and with 100% grade to boot.
If I had to compare the intro courses from Udacity and Coursera, I would still recommend Udacity to start and then use Coursera to augment and deepen the understanding of the basics. I had quite a few “aha!” moments when taking Coursera’s course, but Udacity makes ML more practical and attainable. I thought it demystified the Machine Learning field. After taking the course, you see the application opportunities and the landscape which you should further study. Perhaps best is to combine the two classes – they are drastically different – and learn and compare the concepts between the two.
What’s next? Feb 16th I am starting AI Engineer Nano Degree on Udacity. The same feeling again, a bit daunting and challenging. Hopefully, I will hang in there and power through it. I am sure to post the update as I go.
Before the course starts, I am taking a quick detour back to stats and statistical analysis, to make sure I grasp the basic concepts of analyzing data. Trying to go deeper into kernels and data sets on kaggle.com, familiarizing myself with pandas framework, etc. Basically having fun before AI degree ruins it all.