Getting to Know Machine Learning Technology
Published: 2021-08-06 13:29:53

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History of Machine Learning

Since the first time computers were created, humans have been thinking about how computers can learn from experience. This is evident in 1952, Arthur Samuel created a program, game of checkers, on an IBM computer. The program can learn the moves to win the checkers game and store those moves in its memory.

The term machine learning is basically a computer process to learn from data (learn from data). Without data, computers will not be able to learn anything. Therefore, if we want to learn machine learning, we will definitely continue to interact with data. All machine learning knowledge is bound to involve data. The data may be the same, but the algorithms and approaches are different to get optimal results.

Learning Machine Learning

Machine Learning is a branch of the discipline of Artificial Intelligence (Artificial Intelligence) which discusses the development of systems based on data. Many things are learned, but basically there are 4 main things that are learned in machine learning.

1. Guided Learning (Supervised Learning)

2. Unsupervised Learning

3. Semi Directed Learning (Semi-supervised Learning)

4. Reinforcement Learning

To find out more about Machine Learning, you can take courses on Coursera with instructor professor Andrew NG from Stanford University.

Machine Learning Apps

Examples of the application of machine learning in life are as follows.

1. Application in the field of medicine, for example, is to detect a person's disease from the existing symptoms. Another example is detecting heart disease from an electrocardiogram recording.

2. In the field of computer vision, an example is the application of facial recognition and face labeling such as on Facebook. Another example is the translation of handwriting into text.

3. In the case of information retrieval, examples are language translation using a computer, converting voice to text, and filtering spam emails.

One of the machine learning application techniques is supervised learning. As discussed earlier, machine learning without data will not work. Therefore, the first thing to prepare is the data. The data will usually be divided into 2 groups, namely training data and testing data. The training data will be used to train the algorithm to find a suitable model, while the testing data will be used to test and determine the performance of the model obtained at the testing stage.

From the model obtained, we can make predictions which are divided into two types, depending on the type of output. If the prediction results are discrete, it is called a classification process. For example, gender classification is seen from handwriting (output of male and female). Meanwhile, if the output is continuous, it is called a regression process. For example, the prediction of the range of house prices in the city of Bandung (output in the form of house prices).

Impact of Machine Learning on Society

The application of machine learning technology inevitably has been felt now. There are at least two opposing impacts of the development of machine learning technology. Yes, positive impact and negative impact.

One of the positive impacts of machine learning is that it becomes an opportunity for entrepreneurs and technology practitioners to continue working in developing machine learning technology. The assistance of activities that must be carried out by humans is also one of the positive impacts of machine learning. For example, there is a spell checking feature for each language in Microsoft Word. Checking manually would take days and involve a lot of effort to get a perfect write up. But with the help of the spell checking feature, in real-time we can see errors that occur when typing.

But besides that there are negative impacts that we must be aware of. The existence of labor cuts because jobs have been replaced by machine learning technology tools is a problem that must be faced. Coupled with dependence on technology will be increasingly felt. Humans will be more complacent by the ability of their gadgets so that they forget to learn to do an activity without the help of technology.

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