Machine Learning
Machine Learning is the science of making computers learn and act like humans by feeding data and information without being explicitly programmed. It is a data- driven technique. The algorithms are taught with the help of the training data. With the new data, the predictions and decisions can be made based on the past data.
Introduction
Categories of Machine Learning:
1. Unsupervised Learning
2. Supervised Learning
Explanation
When to use Machine Learning?
1. When there are no human experts, so the data cannot be labelled or categorized.
2. Problems where there are human experts, but it is very hard to define the rules.
3. Problems where there are human experts and the rules can be defined, but where it is not cost effective to implement.
4. When one wants to detect patterns, structures, trends, etc. in the data or to make predictions about the future data and make the decisions.
Examples
Finding a rule
Source: https://www.simplilearn.com/tutorials/machine-learning-tutorial/supervised-and-unsupervised-learning#difference_between_supervised_and_unsupervised_learning
Outgoing relations
- Machine Learning is part of Data Modelling
Incoming relations
- Supervised Learning is a kind of Machine Learning
- Unsupervised Learning is a kind of Machine Learning
- Data Analysis Workflow is part of Machine Learning
- Integration is part of Machine Learning
- Random Forest is part of Machine Learning