2. Types of Machine Learning
Types of Machine Learning
Machine learning has three types :
- Regression:
The output variable to be predicted is a continuous variable. Regression is related to continuous data (value functions). In Regression, the predicted output values are real numbers. It deals with problems such as predicting the price of a house or the trend in the stock price at a given time, etc.
e.g., the score of a student on a subject.
- Classification:
The output variable to be predicted is a categorical variable. It refers to taking an input value and mapping it to a discrete value. In classification problems, our output typically consists of classes or categories. This could be things like trying to predict what objects are present in an image (a cat/ a dog) or whether it is going to rain today or not.
e.g., classifying incoming emails as spam or ham.
- Clustering:
Splitting the dataset into groups based on similarity. No predefined notion of a label is allocated to the groups/clusters formed, e.g., customer segmentation.
Supervised Vs Unsupervised Learning
1 Supervise Learning
In supervised learning, having a full set of labeled data while training an algorithm. Fully labeled means that each example in the training dataset is tagged with the answer the algorithm should come up with on its own.
Example:
Labeled dataset of flower images such as: roses, daisies and daffodils.
When a new image will be fed to model , the model compares it to the training examples to predict the correct label.
2. Unsupervised Learning
Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with the unlabelled data.
Unsupervised learning algorithms allow you to perform more complex processing tasks compared to supervised learning. Although, unsupervised learning can be more unpredictable compared with other natural learning deep learning and reinforcement learning methods.
