In the post we will show how to generate model data and load standard datasets using the sklearn datasets module. We use sklearn.datasets in the Python 3.
In this post we’ll show how to make a linear regression model for a data set and perform a stochastic gradient descent in order to optimize the model parameters. As in a previous post we’ll calculate MSE (Mean squared error) and minimize it.
In this post we’ll share with you the vivid yet simple application of the Linear regression methods. We’ll be using the example of predicting a person’s height based on their weight. There you’ll see what kind of math is behind this. We will also introduce you to the basic Python libraries needed to work in the Data Analysis.
The iPython notebook code
In the post we share some basics of classification and clustering in Machine learning. We also review some of the cluster analysis methods and algorithms.
Often we see “invalid data”, “clean data”, “normalize data”. What does it mean as to practical data extraction and how does one deal with that? One shot is better than 1000 words though:
Finding the most similar sentence(s) to a given sentence in a text in less than 40 lines of code 🙂
In this post, we’d like to share some of the most interesting terms that are used in today’s science and IT world. We think you will benefit from getting familiar with these modern tech-age expressions.