Brandon Rohrer is a Staff Machine Learning Engineer at LinkedIn. Brandon's specialty is creating algorithms and computational methods. You can find examples of his work here.
This post is about working with a mixture of color and grayscale images and needing to transform them into a uniform format - all grayscale. We'll be working in Python using the Pillow, Numpy, and Matplotlib packages.
My goal here is to give you a map for navigating the sprawling terrain of data science. It’s to help you prioritize what you want to learn and what you want to do, so you don’t feel lost.
What follows is a set of broad recommendations, and it will inevitably require a lot of adjustments in each implementation. Given that caveat, here are our curriculum recommendations.
Python's datetime package is a convenient set of tools for working with dates and times. With just the five tricks that I’m about to show you, you can handle most of your datetime processing needs.
The error function expresses how much we care about a deviation of a certain size. The choice of error function depends entirely on how our model will be used.
When we are building a model, we are making the assumption that our data has two parts, signal and noise. Signal is the real pattern, the repeatable process that we hope to capture and describe. The noise is everything else that gets in the way of that.
Models are useful because they allow us to generalize from one situation to another. When we use a model, we’re working under the assumption that there is some underlying pattern we want to measure, but it has some error on top of it.
Video is a natural way for us to understand three dimensional and time varying information. Read this short post on how to achieve the creation of videos from still images.
The goal of this post is identify a single strategy for pulling data from a DataFrame using the Pandas Python library that is straightforward to interpret and produces reliable results.
Data Science Curriculum Roadmap
Pandas DataFrame Indexing