It seems that machine learning is mixed with lots of similar, yet different things. Another one is statistical learning. These two distinct from each other subtly and often their models can also blend, mostly because machine learning can also work on statistical data.

**So what are the distinctions between machine learning and statistical learning?**

### Operating mechanisms

While machine learning uses algorithms and is highly automated (people are less involved in the process), statistical learning requires human factors and is based on equations. In the first case, a large amount of data is not a problem. The second one includes smaller sets of information.

### Approach to tasks

Solving issues using machine learning and statistical learning is quite different. Basically, if you need to build a particular program that will be responsible for an action, each of these systems will have its own direction. Machine learning will be based on an algorithm that can make predictions and classify new pieces of data – other than the original input. Statistical learning will find out dependencies between two factors – like movie preferences of a specific age group. It can help with understanding and stating facts about a concrete phenomenon.

### Outcome

And the result is the main difference between those two. Machine learning can predict, while statistical learning gives an insight into the current state. Statistics conclude facts from numbers and pieces of information. If we need to know what the tendencies are going to be in the future, that won’t be helpful. Using machine learning to discover what can be the pattern is much more useful in this case.