The art and science of :
- Giving Computers the ability to learn
- To make decisions from data
- Without being explicitly programmed
- Learning to predict whether an email is spam or not.
- Clustering Wikipedia entries into different categories
Types Of Machine Learning :
Use Labeled Data
-> Predictor variables/features and a target variable
-> Aim : Predict the target variable, given the predictor variables.
1. Classification : Target variable consist of Categories
2. Regression : Target variable is Continuous.
-> Naming Conventions
1. Features = Predictor Variables = Independent Variables
2. Target Variable = Response Variables = Dependent Variables
-> Examples of Supervised Learning :
1. Automate time-consuming or expensive manual tasks.
-> Ex: Doctor's diagnosis
2. Make Predictions about the future.
-> Ex: Will Customer click on an Ad or not.
3. Need Labeled Data
-> Historical data with labels
-> Experiments to get labeled data
-> Crowd - Sourcing labeled data
Uses Unlabeled data
- Making hidden patterns from unlabeled data
- Example : Grouping customers into different categories (Clustering)
Software agents interact with an environment.
- Learn how to optimize their behaviour.
- Given a system of rewards and punishments.
- Draw inspiration from behavioral psychology.
3. Game Playing
-> Alpha Go : First computer to defeat the world champion in Go.
To get started with Machine Learning here is the path to get started : https://mlait.tech/learning-path-to-machine-learning/