Machine Learning is all over the place and rightly so. With the tremendous amount of data, algorithms and computing power that is available at our disposal today, we are beginning to see a clear shift where the tools and services are now available to all developers (individuals and organizations).
It is not easy to get going with Machine Learning and the task is not to be underestimated. You need expertise in several areas coupled with a mentality that combines persistence and dedication to solving the problem (and in some cases your final results will still conclude that the experiment failed). In addition to developer chops, you need skills in data processing, statistics and an understanding of a particular Machine Learning platform that you plan to use.
Often the difficult part is to kick start your understanding of this domain. I know a bit of machine learning and have used the Google Prediction API on a project and as a student and teacher, I am always on the lookout to see how anyone approaches the very difficult task of explaining a complex subject to a general audience. In my opinion, this is very difficult. People often conclude that the talk was very basic but I can challenge anyone to explain a complex topic in a few minutes that at least gives the high level picture very clearly and then leaves it to the student (who is hopefully curious by now) to take it to the next level.
One such introduction that I came across this week was at Pluralsight in a course titled Understanding Machine Learning by David Chappell. This is a 40 minute video course only (yes 40 minutes!) and it has a great introduction to the subject that I thought could be understood by anyone remotely connected with the software industry. The key processes and terms were explained via short and concise examples that drove home the point.
Take a look at it if you can. If you wanted to get the basics on what ML is all about, the processes and what it involves, this is a great introduction. It should get you curious enough to then start exploring ML libraries/tools in a language of your choice.
P.S: The 40 minute introduction is good enough for you to actually start understanding the image that you see in this blog post.