Choosing the right algorithm for modeling data is a crucial part of the work of a data scientist. Here are the basic techniques.
In order to build a working data model, you'll need to understand all the basics of data access, blending, cleansing and validation.
When you can’t run an actual experiment, introduce pseudo-randomness.
You shouldn’t blindly follow every decision it makes. You must combine data with logic and business sense to make the best decisions.
Log odds, the baseline of logistic regression, explained.
Before analyzing your data and building your model, you must first plot the data set. Anscombe’s quartet shows us why.
Learn how wavelet transforms work and how to do it yourself in this step-by-step tutorial.
You don’t always need a lot of data to train your machine learning model. Here’s how to do it with just a few images per class.
Standard deviation measures the dispersion of data in relation to the mean, while standard error indicates the precision of the estimate of the sample mean. Here’s how to find them.
