Preparing Your Data to be Explored
This is the second post in our Data Exploration with Python series. Before reading this post, make sure to check out Data Exploration with Python, Part 1!
Mise en place (noun): In a professional kitchen, the disciplined organization and preparation of equipment and food before service begins.
When performing exploratory data analysis (EDA), . . .
Preparing Yourself to Become a Great Explorer
Exploratory data analysis (EDA) is an important pillar of data science, a critical step required to complete every project regardless of the domain or the type of data you are working with. It is exploratory analysis that gives us a sense of what additional work should be performed to quantify and extract insights from our data. It also . . .
Visualizing Text with Python
In this article, we explore two extremely powerful ways to visualize text: word bubbles and word networks. These two visualizations are replacing word clouds as the defacto text visualization of choice because they are simple to create, understandable, and provide deep and valuable at-a-glance insights. In this post, we will examine how to . . .
Visual Evaluation and Parameter Tuning
Welcome back! In this final installment of Visual Diagnostics for More Informed Machine Learning, we'll close the loop on visualization tools for navigating the different phases of the machine learning workflow. Recall that we are framing the workflow in terms of the . . .
Demystifying Model Selection
Note: Before starting Part 2, be sure to read Part 1!
When it comes to machine learning, ultimately the most important picture to have is the big picture. Discussions of (i.e. arguments about) machine learning are usually about which model is the best. Whether it's logistic regression, random forests, Bayesian methods, support vector . . .
How could they see anything but the shadows if they were never allowed to move their heads?
— Plato The Allegory of the Cave
Python and high level libraries like Scikit-learn, TensorFlow, NLTK, PyBrain, Theano, and MLPY have made machine learning accessible to a broad programming community that might never have found it otherwise. . . .
Discrete events pervade our daily lives. These include phone calls, online transactions, and heartbeats. Despite the simplicity of discrete event data, it’s hard to visualize many events over a long time period without hiding details about shorter timescales.
The plot below illustrates this problem. It shows the number of website visits made . . .