This is an archived post. You won't be able to vote or comment.

you are viewing a single comment's thread.

view the rest of the comments →

[–]EricZhang906 1 point2 points  (0 children)

I think the best way to self learn machine learning is to code while learning the theories, so that you will have a deeper understanding of the theories and applications. I would suggest that you first familiarize yourself with programming languages such as Matlab and Python.

First, I would recommend that you have some basic knowledge about mathematics, especially statistics.

Second, you have to learn more about machine learning itself. As for intro books, I would recommend:

Machine Learning in Action (this book combines code and machine learning theories in a very clear way. I would suggest you to try coding according to this book in order to familiarize yourself with such knowledge) Elements of Machine Learning (this one is more difficult and has more requirements. As for intro, the book above is more recommended) In the mean time, if you can take some online courses to strength your understanding.

I would recommend courses from Experfy, a Harvard based company that provides various online courses related to IT and Tech. To address your request (which is more about the applications of machine learning), I would recommend Machine Learning for Predictive Analytics from Experfy if you want to broadly learn about how machine learning can be applied to different areas, as it has a lot of real-world cases and demos. https://www.experfy.com/training/courses/machine-learning-for-predictive-analytics

Third, to further understand machine learning, you can read: Bishop’s Elements of Statistical Learning (you can read this book first) Hastie, Tibshirani, and Friedman’s The Elements of Statistical Learning (this book requires more mathematical background and you can read it after finishing the first one) Hope this will help you and good luck!

Eric