Artificial Intelligence (AI)
Machine learning (ML) is artificial intelligence based on creating algorithms that can process data and learn independently. The entire approach is based on the fact that it is more efficient to teach a computer to learn, rather than to program it to perform all the required tasks that are part of a larger goal.
There are countless uses for machine learning and it's easy to find a few examples that are growing in popularity. The first is the rise of the virtual assistant, such as Google Assistant or Apple Siri. These systems use learning algorithms to refine or personalize the results of individual user requests. As the system learns more about the user's habits, it can better handle requests that contain ambiguity.
Another popular application is facial recognition, where a still image can be used as input into a system that identifies the people depicted. Social media services such as Facebook are able to analyze photos and recognize friends in a photo. For example, similar algorithms are used to search and propose people you may know.
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Deep learning (DL) are machine learning algorithms that use neural networks to solve problems. A neural network is a framework of different machine learning algorithms with the aim of solving issues that have yet to be defined. A deep learning system is essentially a very large neural network that is trained using a very large amount of records/data.
There are different types of deep learning architectures and it is common to use a recurrent neural network (RNN) or a convolutional neural network (CNN). The word "deep" refers to the number of layers or transformation points included in the framework. As the input moves through these layers, it becomes more abstract and ends in the output layer. It is at this stage that a prediction is made based on the original input.
Deep learning is currently often used in complex tasks. A well-known example is Google Translate, which can translate written text into more than 100 languages. Looking ahead, deep learning technologies will be applied in top sectors such as: the IT sector, financial sector, energy sector, commercial sector, agricultural sector and health sector.
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Data science (DS) encompasses several data disciplines. Usually this refers to things related to working with big data. The primary main purpose of this is to structure this data and make it usable to work with.
Gaining insight is a process that consists of several stages. This will often involve collecting and processing large amounts of records/data.
Once the data/data structuring is complete, it is also fully usable to perform predictive analytics using tools such as machine learning algorithms, deep learning algorithms, and the neural networks.
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