Till now you have learnt to explore and get insights out of data. Now we will be having a look at measuring relationships between variable through modeling.
Modeling uses machine learning algorithms, in which the machine learns from the data just like humans learn from their experiences.
Let’s go deeper into the types of models that come under machine learning.
Machine learning models can be classified into the following three types based on the task performed and the nature of the output:
- Regression: The output variable to be predicted is a continuous variable, e.g., the score of a student on a subject.
- Classification: The output variable to be predicted is a categorical variable, e.g., classifying incoming emails as spam or ham.
- Clustering: No predefined notion of a label is allocated to the groups/clusters formed, e.g., customer segmentation.
You will get to know about the different types of machine learning models in the coming modules.
Let’s now learn about supervised and unsupervised learning methods.
So, you can classify machine learning models into two broad categories:
- Supervised learning methods
1. Past data with labels is used for building the model.
2. Regression and classification algorithms fall under this category. - Unsupervised learning methods
1. No predefined labels are assigned to past data.
2. Clustering algorithms fall under this category.
Mentioned below are some of the problems that are addressed by either a supervised or an unsupervised learning algorithm. For each problem, identify if the task can be addressed by a supervised learning algorithm or an unsupervised learning algorithm. Assume that an appropriate data set is available for your algorithm to learn from.
In the next segment, you will learn about linear regression in a bit more detail. You will be introduced to the concept of a regression line.