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About I. The Types of Machine Learning
This document discusses different types of machine learning problems and algorithms. It begins by using handwritten digit recognition as an example problem to introduce supervised learning. Unsupervised learning is described as discovering structures in data without labels. Reinforcement learning involves an agent receiving feedback in an environment over time. The document focuses on supervised classification problems and algorithms, using examples like identifying fake Twitter accounts and digit recognition. It emphasizes the importance of training models on a dataset and evaluating them on a separate test set to avoid overfitting. Unsupervised learning is discussed as discovering patterns in unlabeled data.
Detailed Information
| Author: | ['Welluzani Manda'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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