Machine Learning Essentials:Part 1
What is Machine learning?
The scientific field dedicated to developing various algorithms and techniques to enable computers to learn in a manner similar to humans is known as machine learning. Regression is used for numerical problems, whereas problems involving dependent variables are categorized as classification problems
Example of Regression: house prices
Example of Classification:Titanic survived or died.
Supervised Learning:
If our dataset includes labels (target), it is supervised learning. It is based on learning the relationship between dependent and independent variables, aiming to predict the target of new features.
Unsupervised Learning:
If there is no target in the dataset, it is unsupervised learning. Techniques like clustering (segmentation) can be applied.
Reinforcement Learning:
Imagine a robot in an empty room trying to exit. It is reinforcement learning. It involves the robot being punished for each wrong move and learning to navigate the room through trial and error, reinforcing successful actions.
