This Article expands on Simple Linear Regression and Multiple Linear Regression, ensure you have a good understanding of these two topic areas before continuing
This Article expands on [Simple Linear Regression_](https://medium.com/ai-in-plain-english/linear-regression-in-python-part-1-simple-linear-regression-fae7672ff552) and [Multiple Linear Regression_](https://medium.com/ai-in-plain-english/understanding-multiple-linear-regression-2672c955ec1c), ensure you have a good understanding of these two topic areas before continuing.
Polynomial Regression is used to capture non-linear relationships between variables.
For linear relationships we use Linear Regression.
Take a look at the following graph looking at the Humidity and Pressure *values in *Svged, Hungary. [ Yes i like Weather data :) ]
We can see there is a trend in the data, which is *non-linear *so we use Polynomial Regression
The job of Polynomial regression is to find a suitable relationship between Humidity and Pressure, such as the following:
We can then use the model produced by polynomial regression to make suitable predictions for Humidity given any Pressure value.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Statistics for Data Science and Machine Learning Engineer. I’ll try to teach you just enough to be dangerous, and pique your interest just enough that you’ll go off and learn more.
In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.