I’m going deeper in my learning about how to successfully implement machine learning algorithms this year by initally doing a survey of all the resources out there for learning this stuff.
It is a fast moving area of expertise and as such newer techniques and tools wont be covered in older books or tutorials.
MOOCs are now a great way to get up to speed on the Deep Learning approaches to Machine Learning. And while there are some good quality general books out there about ML, most are currently in pre-order.
The most appealing to me right now is the course on Udacity, presented by Google which uses Tensorflow in iPython notebooks to teach how to build and apply ML. The best thing is that it’s
- in Python
- uses the latest ML library TensorFlow (developed at Google)
- Is free
As with all learning, the best way to learn is by doing it yourself and practicing enough to make it stick.
This is not the first resource I’ve used to learn about topics in Machine Learning and it won’t be the last. Taking multiple courses, reading multiple books and tackling multple problems on your own is the best way to ensure you have no gaps and a well rounded deep understanding of the concepts.
Actually mastering a new skill is hard and there are no shortcuts. Accept that and jump into the challenge.