Introduction Feature Selection is the process of determining the most suitable subset of the total number of available features for modeling. It helps to understand which features contribute most to the target data. This is usefull to Improve Model Performance. Redundant and irrelevant features may be misleading for the model. Additionally, if the feature space is too large compared to the sample size. This is called the curse of dimensionality and may reduce the model’s performance.

Introduction Logistic Regression is a Supervised Machine Learning algorithm, in which a model is developed, that relates the target variable to one or more input variables (features). However, in contrast to Linear Regression the target (dependent) variable is not numerical, but categorical. That is the target variable can be classified in different categories (e.g.: ’test passed’ or ’test not passed’). An idealized example of two categories for the target variable is illustrated in the plot below.

Introduction In a previous article, we introduced Linear Regression in detail and more generally, showed how to find the best model and discussed its chances and limitations. In this post, we are looking at a concrete example. We are going to calculate the slope and the intercept from a Simple Linear Regression analytically, looking at the example data provided in the next plot. Illustration of a simple linear regression between the body mass and the maximal running speed of an animal.

Introduction Linear Regression is a type of Supervised Machine Learning Algorithm, where a linear relationship between the input feature(s) and the target value is assumed. Linear Regression is a specific type of regression model, where the mapping learned by the model describes a linear function. As in all regression tasks, the target variable is continuous. In a linear regression, the linear relationship between one (Simple Linear Regression) or more (Multiple Linear Regression) independent variable and one dependent variable is modeled.

In this article we will learn what Deep Learning is and understand the difference to AI and Machine Learning. Often these three terms are used interchangeable. They are however not the same. The following diagram illustrates how they are related. Relation of Artificial Intelligence, Machine Learning and Deep Learning. Artificial Intelligence. There are different definitions of Artificial Intelligence, but in general, they involve computers performing tasks that are usually associated with humans or other intelligent living systems.

Machine Learning In classical programming, the programmer defines specific rules which the program follows and these rules lead to an output. In contrast, Machine Learning uses data to find the rules that describe the relationship between input and output. This process of finding the rules is called ’learning’. Supervised and Unsupervised Learning are two different types of Machine Learning. Let’s discover what each means. Fig. 1: Supervised and Unsupervised Learning are different types of Machine Learning.

Classification Problems Supervised Machine Learning projects can be divided into regression and classification problems. In regression problems, we predict a continuous variable (e.g. temperature), while in classification, we classify the data into discrete classes (e.g. classify cat and dog images). A subset of classification problems is the so-called binary classification, where only two classes are considered. An example of this is classifying e-mails as spam and no-spam or cat images versus dog images.

Regression Problems Regression problems in Machine Learning are a type of supervised learning problem, where a continuous numerical variable is predicted, such as, for example, the age of a person or the price of a product. A special type is the Linear Regression, where a linear relationship between two (Simple Linear Regression or more (Multiple Linear Regression) is analyzed. The example plots in this article will be illustrated with a simple linear regression.

Introduction When we think about Data Science, we usually think about Machine Learning modeling. However, a Data Science project consists of many more steps. Whereas modelling might be the most fun part, it is important to know that this is only a fraction of the entire lifecycle of a Data Science project. When we plan a project and communicate how much time we need, we need to make sure that enough time is given for all the surrounding tasks.