Machine learning

Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

These are three types of machine learning: 

• supervised learning,
• unsupervised learning, and
• reinforcement learning.

Supervised Learning

This algorithm consists of a target/outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using this set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data.

Examples of Supervised Learning:

• Regression, 
• Decision Tree,
• Random Forest,
• KNN,
• Logistic Regression etc.

Unsupervised Learning

In this algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering populations in different groups, which is widely used for segmenting customers into different groups for specific interventions.

Examples of Unsupervised Learning:

• Apriori algorithm,
• K-means.

Reinforcement Learning

Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.

Example of Reinforcement Learning: Markov Decision Process

Applications

• Image recognition.
• Speech recognition.
• Medical diagnosis.
• Statistical arbitrage.
• Predictive analytics.
• Extraction.

Conclusion

If you have lesser amount of data and clearly labelled data for training, opt for Supervised Learning. Unsupervised Learning would generally give better performance and results for large data sets.