Machine Learning Basics
Machine learning is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions. It is based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.
Key Concepts
Supervised Learning:
Definition: In supervised learning, the model is trained on a labeled dataset, which means that each training example is paired with an output label.
Examples: Classification (e.g., spam detection in emails) and regression (e.g., predicting house prices).
Unsupervised Learning:
Definition: Unsupervised learning deals with unlabeled data. The model tries to learn the patterns and the structure from the data without any specific output labels.
Examples: Clustering (e.g., customer segmentation) and association (e.g., market basket analysis).
Semi-Supervised Learning:
Definition: This approach uses both labeled and unlabeled data for training. It typically uses a small amount of labeled data with a large amount of unlabeled data.
Examples: Improving image recognition systems with a few labeled images and many unlabeled ones.
Reinforcement Learning:
Definition: Reinforcement learning involves training models to make sequences of decisions by rewarding them for good actions and penalizing them for bad ones.
Examples: Game playing (e.g., AlphaGo) and robotics (e.g., robot navigation).
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Emma Smith
15th August, 2019 at 01:25 pm