On many occasions, while working with the scikit-learn library, you’ll need to save your prediction models to file, and then restore them in order to reuse your previous work to: test your model on new data, compare multiple models, or anything else. This saving procedure is also known as object serialization – representing an object with a stream of bytes, in order to store it on disk, send it over a network or save to a database, while the restoring […]

Part 1: Collecting Data From Weather Underground This is the first article of a multi-part series on using Python and Machine Learning to build models to predict weather temperatures based off data collected from Weather Underground. The series will be comprised of three different articles describing the major aspects of a Machine Learning project. The topics to be covered are: Data collection and processing (this article) Linear regression models (article 2) Neural network models (article 3) The data used in […]

This article is a continuation of the prior article in a three part series on using Machine Learning in Python to predict weather temperatures for the city of Lincoln, Nebraska in the United States based off data collected from Weather Underground’s API services. In the first article of the series, Using Machine Learning to Predict the Weather: Part 1, I described how to extract the data from Weather Underground, parse it, and clean it. For a summary of the topics […]

What is a Neural Network? Humans have an ability to identify patterns within the accessible information with an astonishingly high degree of accuracy. Whenever you see a car or a bicycle you can immediately recognize what they are. This is because we have learned over a period of time how a car and bicycle looks like and what their distinguishing features are. Artificial neural networks are computation systems that intend to imitate human learning capabilities via a complex architecture that […]