Machine Learning

What is Machine Learning?

Machine learning is the study and development of computational algorithms that analyze data and employ statistical methods to achieve predictions and conclusions. This is an application of artificial intelligence.

These algorithms are capable of learning from experience without being explicitly programmed to do so and can greatly reduce human intervention and assistance. Machine learning enables the analysis of massive amounts of data; it generally produces faster, more accurate results from this analysis than the ones obtained from traditional data manipulation methods.

Characteristics of Machine Learning

The science of Machine Learning relies heavily on computational statistics. These algorithms are expected to derive conclusions and predictions without being explicitly programmed to calculate that result. Machine learning depends on the capacity of a machine to learn from data that is fed into it, identify certain patterns and function autonomously.

There is a correlation between the amount and quality of the data you are feeding into the system, and the accuracy of the prediction produced. Your system will learn more from additional data.

You can find machine learning usages in your daily life. For example, if you need to install a video surveillance system to cover a great area, it will be hard to have a controller watching all the cameras simultaneously. You can use an algorithm that learns what is the normal behaviour and movement demonstrated by a human in that area. If the person is behaving in an unusual way, in a manner which the machine does not recognize, the human controller will be alerted and may check the source of the disturbance. The great asset of these algorithms is that they are not programmed to respond if a person does a specific action; they are actually and continuously learning what are the normal behaviours and adapting their response.

What are the benefits of Machine Learning?

Machine learning improves the accuracy of traditional prediction models.
Increases the capacity to identify trends and patterns not evident to humans.
These algorithms are self-improving, both in accuracy and efficiency.
It is capable of handling multi-dimensional data.

When to use Machine Learning

Machine learning can be used in a wide range of applications to improve tasks like the identification of profitable opportunities or dangerous risks. A rapidly growing number of industries is implementing it.

In Finance, it is used to identify investment opportunities, create accurate market models, and prevent fraud. In manufacturing these algorithms can be implemented to create the best maintenance models, predicting equipment life cycles, and optimizing intervention’s cost and timing. There are already various applications in healthcare, like algorithms that predict illnesses and treatment needs in certain population groups, by identifying patterns and modelling disease progression. This helps physicians and investors make better decisions and anticipate their intervention.

Things to note before using Machine Learning

A machine learning algorithm needs massive amounts of data to train and learn, and this data must have some quality – for example, it should be unbiased. After collecting and feeding this data there is a learning and development period before the system is ready to work with the required accuracy.

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