This is probably one of the best times to learn and practice machine learning. The abundance of high quality resources is incredible. Really, I don't think I can stress this enough. The only requirement is time and the ability to go through discomfort and put on the effort that is required for learning and developing any skill; many of the resources are free, so, there is no excuse. This is what I tell myself when I'm lured into procrastination.
The one that I'm gonna suggest today is Intro to Machine Learning from Udacity, which is an intermediate level course taught by Sebastian Thrun, a pioneer in the field, and Katie Malone. The course is free. If you seek recognition and credentials for finishing the course, you'd probably have to enroll in the Data Analyst nano-degree.
Here's some of what's included inside:
- Naive Bayes algorithms
- Support Vector Machines
- Decision Trees algorithms
- Regressions
- Clustering
- Feature Scaling
- and more.
Some of the prerequisites include:
- knowledge of computer science
- Python programming
- inferential statistics
- descriptive statistics
So, despite the title, in my view, this is not really for beginners.
What I like a lot is their approach to teaching the course. Each module is divided into many very short video lessons, each discussing a very specific topic. So, it gets very 'granular', if I can put it that way; added all together, there are more than 490 of these small videos that make up the course.
Atop of that, another favorite aspect for this course is that it's very interactive, requires your immediate input in almost all videos, and this makes the learning process much faster. Enough said, enroll if you think this is for you!
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Cristi Vlad, Self-Experimenter and Author