Machine learning is a branch of artificial intelligence. Machine learning algorithms allow the system to automatically learn and improve itself without the need to be explicitly programmed. Machine learning focuses on developing computer programs capable of accessing data and using it for self-learning.
If you majored in statistics, mathematics, and data mining in college, you can use your knowledge to explore machine learning. Having a strong foundation in these three branches of pure science can be the basis for becoming a machine learning engineer.
The machine learning process starts from observing data, for example, direct experience or instructions whose purpose is to look for data patterns, study them, to be used as a basis for better decision making in the future. The key is to make the computer/machine capable of learning automatically without human intervention/assistance and then adjusting the appropriate action accordingly.
In short, machine learning has the ability to obtain data based on its own orders, not human orders. The data obtained is then studied by machine learning to then carry out certain tasks. The tasks that machine learning can perform also vary, depending on what it is learning. In this way, machine learning algorithms make machines work like humans.
The term machine learning first appeared around the 1920s by several mathematical scientists, namely Adrien Marie Legendre, Thomas Bayes, and Andre Markov. At that time they presented the basics of machine learning and its concepts. Since then machine learning has continued to be developed until now.
Today, machine learning is commonly used to help human jobs. We can also see the application of machine learning technology today because many objects around have used this technology. Examples of machine learning that are around us for example:
And many others.
Maybe you are wondering, how is it possible for machines to learn/learn something and then make their own decisions?
The answer is because machine learning is programmed to be able to learn on its own and analyze data based on data at the beginning of development and data during the use of machine learning itself.
Machine learning works according to the techniques or methods used during development. There are two techniques in Machine Learning, namely:
Supervised Machine Learning algorithms can apply what has been learned in the past to new data using labeled examples to predict the future. Starting from the analysis of the dataset, the algorithm can produce inferred functions for making predictions.
The system is able to provide targets for any input after adequate training. The learning algorithm is also able to compare the output results with the correct and desired output, then it is able to detect errors to modify the model accordingly.
In contrast to Supervised Machine Learning, Unsupervised Machine Learning is used when the information used to train the machine is not classified or labeled. Unsupervised Machine Learning studies how the system can deduce functions to describe the hidden structure of data without labels.
Machine learning may not find the correct output, but the machine is able to explore the data to draw conclusions from the data set, to describe the hidden structure of the data without labels.
Machine learning enables machines to analyze massive amounts of data, and provide fast and accurate results. Machine learning can identify profitable opportunities or find dangerous risks, so today’s machine learning is important for business or for other matters relating to life.
However, it takes time to train machines to capable of making fast and accurate results and/or decisions. Are you interested in pursuing a career as a machine learning engineer now? It is said that machine learning will be on further development for some time ahead and therefore, pursuing a career as a machine learning engineer is considered a bright investment for the future.
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