Machine Learning
Description:
- Learn and improve from “experience” without emperically programmed
- Data-driven
- mostly “structured”.
- Once the training phase is over, the adaptive machines can apply their findings to real cases and make predictions, for example. If a machine learning algorithm produces inaccurate assertions, data engineers intervene and make adjustments or corrections.
Types of machine learning method:
- Supervised learning
- Unsupervised learning
- no label
- Large Language Model
- Semi-supervised learning
- Reinforcement learning
- with experience by interacting with the environment
- s
Model evaluation
Tools:
- Training: