I recommend that beginners start with Roger Huang’s 41 questions and answers about Machine Learning. On his webpage Roger Huang provides links to further reading as well.
It is clear that Machine Learning uses many statistical techniques. Therefore, students should prepare themselves by taking applied statistics classes. Statistics first! You can learn programming and network theory later. Learn statistics first.
Other introductory resources on the web
Machine Learning Tutorial for Beginners
An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples
Free university courses
MIT Open Courseware: Machine Learning
MIT Open Courseware is a great service to humanity. Here’s a related course: Artificial Intelligence. Their most visited courses based on the site traffic of the previous month.
Stanford Engineering Everywhere: Machine Learning
Stanford Engineering Everywhere (SEE) is a great project as well. SEE provides free courses online. Videos as well as PDFs are provided. Unfortunately, SEE project was a pilot project and currently no new courses are added.
There is also edX which is a non-profit organization founded by MIT and Harvard University. “The majority of edX courses are entirely free to access and most offer an optional paid verified certificate track with graded assignments and the opportunity to work towards a certificate for a fee that varies per course. The Verified track awards a certificate after you successfully pass the course.”