Machine Learning – Supervised VS Unsupervised Learning

hello in this video we'll provide some basics on supervised and unsupervised learning an easy way to begin grasping the concept of supervised learning is by looking directly at the words that make it up supervised means to observe and direct the execution of a task project or activity obviously we're not going to be supervising a person instead we'll be supervising a machine learning model that might be able to produce classification regions like we see here so how do we supervised a machine learning model we do this by teaching the model that is we load the model with knowledge so that we could have it predict future instances but this leads to the next question which is how exactly do we teach a model we teach the model by training it with some data from a labelled data set it's important to note that the data is labeled and what does a label data set look like well it can look something like this this example is just taken from the iris data set which is a famous data set used for machine learning let's start by classifying some components of this table the names up here which are called siebel length sepal width petal length petal width and species are called the attributes the columns are called features which include the data if you look at a single data point on a plot it'll have all of these attributes that would make a row on this chart or an observation looking directly at the value of data you can have two kinds the first is numerical when dealing with machine learning the most commonly used data is numeric the second is categorical that is it's non numeric because it contains characters rather than numbers in this case it's categorical because this data set is made for classification usually a data set like this will be put into a dot CSV file or comma separated value file this file separates observations by new lines and attributes by commas hence comma separated there are two types of supervised learning classification and regression since we know the meaning of supervised learning what do you think unsupervised learning means unsupervised learning is exactly as it sounds we do not supervise the model but we let the model work on its own to discover information that may not be visible to the human eye unsupervised learning uses machine learning algorithms that draw conclusions on unlabeled data unsupervised learning has more difficult algorithms than supervised learning since we know little to no information about the data or the outcomes that are to be expected with unsupervised learning we're looking to find things such as groups or clusters perform density estimation and dimensionality reduction in supervised learning however we know what kind of data we're dealing with since it is labeled data in comparison to supervised learning unsupervised learning has fewer tests and fewer models that can be used in order to ensure the outcome of the model is accurate as such unsupervised learning creates a less controllable environment as the machine is creating outcomes for us now let's investigate a machine learning algorithm here we can see the output of an algorithm applied to examining poisonous mushrooms as you can see it tells us if a mushroom is poisonous or edible depending on its features so without looking at the data itself do you think this is a supervised or unsupervised machine learning problem the answer is supervised machine learning as this is an example of classification that is it classifies mushrooms into two different labels poisonous or edible specifically it does so using a classification tree algorithm the biggest difference between supervised and unsupervised learning is that supervised learning deals with labelled data while unsupervised learning deals with unlabeled data in supervised learning we have machine learning algorithms for classification and regression classification is the organization of labelled data regression is the prediction of trends in labeled data to determine future outcomes while it's possible to classify data using regression covering that now is out of scope for this course in unsupervised learning we have clustering clustering is the analysis of patterns and groupings of unlabeled data thanks for watching

13 thoughts on “Machine Learning – Supervised VS Unsupervised Learning”

  1. The idea of unsupervised learning could be explained with fruit. While we may not know categories that each belongs to, unsupervised learning will establish common characteristics between each, forming clusters. A common cluster for apples could be they are round and most are green, but only we can determine this.

    The link below has an image at the end of the article that compares facial recognition for supervised and unsupervised learning:

  2. you did not do a good job explaining features. You first said its value, then category, then observation, then a block.

  3. unsupervised learning is a real bitch, still can't get my head around it. Supervised is much better, simpler and enough for most scenarios.

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