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Have you ever seen some cool applications of computer vision tools, like this the one below? Perhaps your phone’s camera can autofocus on faces, or maybe you have uploaded a photo on a social media platform and it automatically recognized the person on the image? These are facial recognition applications and they all rely on Machine Learning. In this post, we are going to use a very easy package called OpenCV to build our own facial recognition program!

Linear and Logistic regression are among the most elementary algorithms for supervised learning. Supervised Learning describes the situation where we deal with labeled data, which means that we have labeled inputs and a target variable. Despite the fact that both have the word “regression” in their name, only one of them is typically being used for solving regression problems! Let’s see how they work! Linear Regression Linear regression is possibly the easiest, most intuitive way of making a quantitative prediction. The relationship between an independent and a dependent variable is assumed to be linear, meaning that the dependent variable can be predicted using a linear function of the independent variable. For example:

After reading this article from Pratap Vardhan with great interest, I wanted to build my own version of a Bar Chart Race that is smoother and a bit more beautiful. The biggest improvement is the interpolation (or augmentation) of the available data points in order to make the animation smoother. Here is the Bar Chart Race we are going to build in this article:

Web Scraping is the automated process of extracting data from websites. This is commonly done by retrieving the HTML code of a website through a request and then extracting the information hidden in the HTML code programmatically. This is especially useful when there is no convenient API available to you! There has been a lot of discussion going on about the legality and ethics of Web Scraping, which I do not want to get into in this article. You can find a lot of information on this online.

The k-means algorithm is used to divide unlabeled data into categories or classes, in order to draw useful conclusions from the resulting clusters. Let’s take a look at an imaginary dataset of n = 18 observations of different coffee brands. Note that we would never actually use the k-means algorithm on such a small dataset. We plot the price of the coffee vs. the rating obtained by customers: