Mastering Numerical Computing with NumPy: Master scientific computing and perform complex operations with ease by Mert Cuhadaroglu & Umit Mert Cakmak

Author:Mert Cuhadaroglu & Umit Mert Cakmak [Cuhadaroglu, Mert]
Language: eng
Format: epub
Tags: COM018000 - COMPUTERS / Data Processing, COM021030 - COMPUTERS / Databases / Data Mining, COM051360 - COMPUTERS / Programming Languages / Python
Publisher: Packt Publishing
Published: 2018-06-27T23:00:00+00:00

Supervised learning and linear regression

Machine learning gives computer systems an ability to learn without explicit programming. One of the most common types of machine learning is supervised learning. Supervised learning consists of a set of different algorithms which formulates a learning problem and solves them by mapping inputs and outputs using historical data. The algorithms analyze the input and a corresponding output, then link them together to find a relationship (learning). Finally, for the new given dataset, it will predict the output by using this learning.

In order to differentiate between supervised and unsupervised learning, we can think about input/output-based modeling. In supervised learning, the computer system will be supervised with labels for every set of input data. In unsupervised learning, the computer system will only use input data without any labels.

As an example, let's assume that we have 1 million photos of cats and dogs. In supervised learning, we label the input data and state whether a given photo is of a cat or a dog. Let's say we have 20 features for each photo (input data). The computer system will know whether the photo is a cat or a dog as it's labeled (output data). When we show the computer system a new photo, it will decide whether it's a cat or a dog by analyzing 20 features of the new photo and make a prediction based on its previous learning. In unsupervised learning, we will just have 1 million cat and dog photos without any labeling stating whether the photo's of a cat or a dog, so the algorithm will cluster the data by analyzing its features without our supervision. After clustering is finished, a new photo will be fed into the unsupervised learning algorithm and the system will tell us which cluster the photo belongs to.

In both scenarios, the system will have a simple or complex decision algorithm. The only difference is whether there is any initial supervision or not. An overview scheme of supervised learning methods is as follows:

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