Hey guys, hope you are doing great. Previously we covered the supervised machine learning algorithms series. This is the introductory blog for unsupervised learning, the differences between supervised and unsupervised and many more things. From the next blog, we will be starting with the unsupervised algorithms.

Unsupervised machine learning algorithms

Machine learning is a powerful tool for solving complex problems in different domains. Machine learning algorithms can be broadly classified into two categories: supervised learning and unsupervised learning.

Supervised Learning

Supervised learning is a machine learning technique that involves training a model using labelled data. Labelled data refers to data that has already been categorized or classified. The goal of supervised learning is to use the labeled data to make predictions or classifications for new, unseen data.

Examples of supervised learning algorithms are:

• Linear regression

• Decision trees

• Neural networks

• Support vector machines (SVM)

• Random forests

Unsupervised Learning

Unsupervised learning is a machine learning technique that involves training a model using unlabeled data. Unlabeled data refers to data that has not been categorized or classified. The goal of unsupervised learning is to identify patterns or structures in the data.

Examples of unsupervised learning algorithms are:

• Clustering

• Principal component analysis (PCA)

• Autoencoders

• Generative adversarial networks (GANs)

• Association rule mining

Key Differences

The key differences between supervised and unsupervised learning are as follows:

1. Labeled vs Unlabeled Data: Supervised learning uses labeled data to train a model, whereas unsupervised learning uses unlabeled data.

2. Predictions vs Patterns: Supervised learning is used to make predictions or classifications for new, unseen data, whereas unsupervised learning is used to identify patterns or structures in the data.

3. Performance vs Exploration: Supervised learning is focused on optimizing the performance of a model on a specific task, whereas unsupervised learning is focused on exploring the data to discover new insights.

Conclusion

Supervised and unsupervised learning are two fundamental techniques in machine learning. Supervised learning is used when there is labeled data available, and the goal is to make predictions or classifications for new data. Unsupervised learning is used when there is no labeled data available, and the goal is to identify patterns or structures in the data. Researchers and practitioners must choose the right technique based on their data and specific application requirements.