Contents:
Definition
Machine learning (ML) could be defined as the computer’s ability to learn how to do a specific task that humans usually perform without needing to be explicitly programmed.
The computer learns by repeating the task repeatedly and improving the result each time until it can do it correctly. To achieve this, it leverages algorithms and statistical models to learn through patterns and inference. These ML algorithms are designed so that they learn and improve when they obtain new data.
Machine learning techniques have proven to be a significant technological advancement, and nowadays, many companies are applying them to accelerate innovation, fuel new growth opportunities, and create efficiencies.
Types
There are many machine learning algorithms, but the following are the two primary methods in use today.
Supervised learning
In supervised learning, the algorithm is trained with labeled data. Then this algorithm finds relationships between the given parameters and the labels. Once the algorithm is trained, it can predict the label for other data, so given all the required parameters, we can obtain a prediction for the label.
An example would be an algorithm trained with different variables of certain houses (such as square meters, number of bedrooms, bathrooms, etc.) and their respective prices. With this algorithm, it would be possible to predict a house’s price given the required variables.
The next table shows an example of a dataset of house prices:
| date | bedrooms | bathrooms | sqft_living | sqft_lot | … | price |
|---|---|---|---|---|---|---|
| 02/05/2014 0:00 | 3 | 1.5 | 1340 | 7912 | … | 313000 |
| 02/05/2014 0:00 | 5 | 2.5 | 3650 | 9050 | … | 2384000 |
| 02/05/2014 0:00 | 3 | 2 | 1930 | 11947 | … | 342000 |
| 02/05/2014 0:00 | 3 | 2.25 | 2000 | 8030 | … | 420000 |
| 02/05/2014 0:00 | 4 | 2.5 | 1940 | 10500 | … | 550000 |
| … | … | … | … | … | … | … |
In the table, we can see the label variable in the last column, specifically the house price. All other columns are variables that are used to predict this price.
Unsupervised learning
In unsupervised learning, there is no label.
Applications
Benefits
Conclusions
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