Cloud Hosting

Understanding Machine Learning

The term Machine Learning seems to be a hot cake these days. So, what exactly is it?Well, simply put, Machine Learning is the sub-field of Artificial Intelligence, where we teach a machine how to learn, with the help of input data.Now that we know, what exactly is machine learning, lets have a look at the types of Machine Learning algorithms.

Machine Learning Algorithms can be grouped into two types:

Learn Machine Learning from experts, click here to more in this Machine Learning Training in London!

In supervised machine learning algorithms, we have input variables and output variables. The input variables are denoted by x and the output variables are denoted by y.Here, the aim of supervised learning is to understand, how does y vary with x, i.e. the goal is to approximate the mapping function so well that when we have a new input data (x) we can predict the output variables (Y) for that data.Or, in other words, we have dependent variables and independent variables and our aim is to understand how does a dependent variable change with respect to an independent variable.Lets understand supervised learning through this example:Here, our independent variable is Gender of the student and dependent variable is Output of the student and we are trying to determine whether the student would pass the exam or not based of the students gender.Now, supervised learning can again be divided into regression and classification, so lets start with regression.

In regression, the output variable is a continuous numeric value. So, lets take this example to understand regression better:Here, the output variable is the cost of apple, which is a continuous value, i.e. we are trying to predict the cost of apple with respect to other factors.Now, its time to look at one of the most popular regression algorithm -> Linear Regression.

For the best of career growth, check out Intellipaats Machine Learning Course and get certified.

As the name states, linear regression is used to determine the linear relationship between independent and dependent variable. Or in other words, it is used in estimating exactly how much ofywill linearly change, whenxchanges by a certain amount.As we see in the image, a cars mpg(Miles per Gallon) is mapped onto the x-axis and the hp(Horse Power) is mapped on the y-axis and we are determining if there is a linear relationship between hp and mpg.So, this was the linear regression algorithm, now lets head onto classification in machine learning.

In classification, the output variable is categorical in nature. So, lets take this example to understand classification better:Here, the output variable is the gender of the person, which is a categorical value and we are trying to classify the person into a specific gender based on other factors.Now, well look at these classification algorithms in brief:

Decision tree is one of the most used machine learning algorithms in use, currently. As the name suggests, in Decision Tree, we have a tree-like structure of decisions and their possible consequences.At each node there is a test condition and the node splits into left and right children based on the test condition.Now, lets look at some terminologies of Decision Tree In Python:

Become Master of Machine Learning by going through this online Machine Learning course in Singapore.

As the name states, random forest is an ensemble of multiple decision tree models. In this algorithm, random subsets are generated from the original dataset. Lets say, if x datasets are created from the original dataset, then, x decision trees are built on top of these datasets. So, each of these decision trees generate a result and the optimal solution is found out by taking the aggregate of all the individual results.

So, these were some of the classification algorithms, now, lets head onto unsupervised learning:

In unsupervised machine learning algorithms, we have input data with no class labels and we build a model to understand the underlying structure of the data. Lets understand this with an example:Here, we have input data with no class labels and this input data comprises of fish and birds. Now, lets build an unsupervised model on top of this input data. So, this will give out two clusters. The first cluster comprises of all the fish and the second cluster comprises of all the birds.

Now, you guys need to keep in mind that even though there were no class labels, this unsupervised learning model was able to divide this data into two clusters and this clustering has been done on the basis of similarity of characteristics.Now, out of all the unsupervised machine learning algorithms, k-means clustering is the most popular, so lets understand that.

Go through this Artificial Intelligence Interview Questions And Answers to excel in your Artificial Intelligence Interview.

K means clustering is an unsupervised machine learning algorithm, where the aim is to group similar data points into a single cluster. So, there must be high intra-cluster similarity and low inter-cluster similarity, i.e. all the data points within a cluster should be as similar as possible and the data points between two different clusters should be as dissimilar as possible.In k-means clustering, k denotes the number of clusters to be formed. So, in the above picture, the value of k=3 and hence 3 clusters are formed.

Read more here:
Machine Learning Algorithms | Machine Learning | Intellipaat