2/14/2017

Attending Stanford Statistical Learning MOOC

I signed up to attend the Stanford Statistical Learning course.  It's free and self paced.  There's a lot to learn.

The course is structured in a series of lectures.


Each lecture, one of the two professors explains various aspects of Machine Learning.

Then you can view the Lecture Slides at your own pace.


Then the R Sessions, as this course uses the R statistical language.


And to get up to speed on the R Statistical Language there are e-books available for download and consumption.


As well as a tab to review your progress, as there are quizzes and the passing score is 50%.  As well as a Discussion tab to see previous questions and answers to course related material.

Overall, I'm enjoying the class so far. 

First, you have to get an understanding of the concepts of Machine Learning.  Linear Models, Classification, Clustering, Supervised and Unsupervised Learning. 

Second, the course uses advanced math to explain how the algorithms work.  If you aren't up to speed on advanced math, it's a bit of a challenge. 

Third, you learn the R Statistical language from reading the e-books.

Fourth, you learn to tie the Machine Learning to Math to Statistics using the R Language in your chosen IDE for the complete picture.

Suffice to say, the course is a challenge even for those working with data for many years.  Perhaps that is why Data Science is a challenge and the number of qualified technical talent is below what's needed.

With that said, I believe Machine Learning is the hot topic today, even surpassing Big Data.  When combined, it's quite an arsenal of skills.  Plus working with data files and databases.  And translating business questions to projects that generate outcomes to produce insights viewable in Data Visualizations.  And the Models generated for re-use and combined with real time calls from web applications to produce statistical probability is kind of huge.

I'm going to continue to go through the course and learn as much as possible.  Realistically, just trying to get the big picture as a foundation, then fill in the holes over time.

And there you have it~!