The Math of Machine Learning

Matrix multiplication diagram
(hover for CC attribution)

One of the challenges of data science in general is that it is a multi-disciplinary field. For any given problem, you may need skills in data extraction, data transformation, data cleaning, math, statistics, software engineering, data visualization, and the domain. And that list likely isn’t inclusive.

One of the first questions when it comes to machine learning in specific, is “how much math do I need to know?”

This is where I would recommend you start, to get the most value for your time:

  • Matrix Multiplication (Subject: Linear Algebra)
  • Probability (Subject: Statistics)
  • Normal Distributions (Subject: Statistics)
  • Bayes Theorem (Subject: Statistics)
  • Linear Regression (Subject: Statistics)

Of course you will run across other math needs, but I think the above list represents the foundation.

If you need places to get started with those topics, check out Kahn Academy, Coursera, or your location library.

For more on machine learning, check out other posts such as ML in R, Linear Algebra in R, and ML w/XGBoost.

Installing TensorFlow on CentOS

Google released TensorFlow as open source for community use and improvement. From the site: “TensorFlowâ„¢ is an open source software library for numerical computation using data flow graphs.”

The instructions on are aimed at Ubuntu and OS X. I had a need to install it on CentOS so I documented the steps in a github gist. Feel free to comment if you find something I missed:

* Updated 8/18/2016 for TensorFlow 0.10
* Updated gist 10/18/2016 to correct typo in epel-release