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Ever since the intersection of lightning-fast hardware and brilliant software, machines have been learning how to think like humans. Data scientists use many different kinds of machine learning algorithms to discover patterns in big data that lead to actionable insights. At a high level, these different algorithms can be classified into two groups based on the way they “learn” about data to make predictions: supervised and unsupervised learning.
- Explain the differences between the two main types of machine learning methods.
- Describe how artificial neural nets (ANNs) use supervised learning to predict outcomes in decision-making.
- Provide one real-world example of how each type of learning is applied in data science.