Machine learning is an application of artificial intelligence (AI). In this way, we give systems the ability to automatically learn from experience. The background to this learning process is that these systems improve automatically without explicitly programming them.
Machine learning focuses on developing programs that access data, process it and then use it for themselves to learn.
The learning process starts from observations or data. You can give instructions to an ML application to look for patterns in provided data. This allows the application to make better decisions in the future when we provide new data, for example.
The main goal of a machine learning application is to make the application mature enough that it learns automatically without the software developer taking any further action on the application.
At this point, you should have a rough overview of what machine learning is and what your main goal is pursuing with it.
We have seen ML being used in various sectors, such as Legal, Healthcare, Agriculture, Transportation, Blockchain, Gambling, Insurance and many more. The list is long and we are impressed how online-kaszino and SAP have successfully implemented machine learning to their products.
Let's take a look at some machine learning methods below.
ML algorithms can be broadly classified into two categories:
- Supervised learning
- Unsupervised learning
These forms of learning can be explained as follows.
Supervised learning algorithms can apply what has been learned in the past to new data. For this, they mainly use bracked examples to make future predictions.
Based on the analysis of a known data set from the model's training process, the learning algorithm creates a derived function to make predictions about the output values.
The system is able to deliver goals for each new entry after sufficient training. The learning algorithm can also compare its output to the correct, intended output and find errors to modify the model accordingly.
In contrast, unsupervised learning algorithms are used when the information used for training is neither classified nor labeled.
Unsupervised learning explores how systems can derive a function from untagged (non-labeled) data to describe a hidden pattern. The system doesn't find the right output, but it does research the data and can draw conclusions from data sets to describe hidden structures from untagged data.
Semi-supervised algorithms for machine learning are a hybrid of supervised and unsupervised learning. The training data contains both bracked and un-abelled data. In practice, you typically take a small amount of labeled data and a large amount of unlabeled data.
Systems that use this method are able to significantly improve learning accuracy. Semi-supervised learning is typically used when the labeled data obtained requires qualified and relevant resources to train or learn from it.
Otherwise, obtaining unmarked data usually does not require additional resources.
Reinforcement machine learning algorithms are a learning method that interacts with their environment. To do so, she carries out actions to detect mistakes or rewards. The trial and error principle as well as delayed reward are the most important features of reinforcement learning.
This method allows applications and developers to automatically determine ideal behavior within a specific context to maximize performance. For the developer to learn which action is best, simple reward feedback called a reinforcement signal is required.
Machine learning enables the analysis of large amounts of data (big data). While it generally delivers faster and more accurate results to identify profitable opportunities or dangerous risks, it can take additional time and resources to train the models properly.
A combination of machine learning combined with other AI competencies will make big data processing even more effective in the future.