Machine Learning and How Does It Work? Lesson 1 of 14By Priyadharshini Last updated on Dec 8, 20201629552 PreviousNext An exciting branch of Artificial Intelligence, Machine Learning is all around us in this modern world. Like Facebook suggesting the stories in your feed, Machine Learning brings out the power of data in a new way. Working on the development of computer programs that can access data and perform tasks automatically through predictions and detections, Machine Learning enables computer systems to learn and improve from experience continuously. As you feed the machine with more data, thus enabling the algorithms that cause it to “learn,” you improve on the delivered results. When you ask Alexa to play your favorite music station on the Amazon Echo, she will go to the one you have played the most; the station is made better by telling Alexa to skip a song, increase volume, and other various inputs. All of this occurring because of Machine Learning and the rapid advance of Artificial Intelligence.
A good start at a Machine Learning definition is that it is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (well data) like humans without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves. In other words, with Machine Learning, computers find insightful information without being told where to look. Instead, they do this by leveraging algorithms that learn from data in an iterative process. At a high level, Machine Learning is the ability to adapt to new data independently and through iterations. Basically, applications learn from previous computations and transactions and use “pattern recognition” to produce reliable and informed results.
The Machine Learning process starts with inputting training data into the selected algorithm. Training data being known or unknown data to develop the final Machine Learning algorithm. The type of training data input does impact the algorithm, and that concept will be covered further momentarily. To test whether this algorithm works correctly, new input data is fed into the Machine Learning algorithm. The prediction and results are then checked. If the prediction is not as expected, the algorithm is re-trained multiple numbers of times until the desired output is found. This enables the Machine Learning algorithm to continually learn on its own and produce the most optimal answer that will gradually increase in accuracy over time. The next section of the 'What is Machine Learning' article discusses the types of Machine Learning.