Machine Learning

You’ve probably heard of the phrase “Machine Learning”, but what does it really mean? At first thought, you may associate machine learning with robots and other advanced technology, but although this is true, machine learning also plays a big role in our everyday lives! Some seemingly simple activities such as Google searches and online shopping are made possible through machine learning along with groundbreaking technologies such as self-driving cars and enhanced health care.

Machine Learning Basics

So, what exactly is machine learning and how does it work? In 1959, computer scientist Arthur Samuel formally stated that machine learning is “the study that gives computers the ability to learn without being explicitly programmed.” In more detailed terms, however, machine learning is an application of artificial intelligence that allows computers to learn from past experiences to predict future behaviors without being explicitly programmed. Let’s take the example of self-driving cars. Machine learning can be used to enhance automotive vehicles’ pedestrian detection systems by providing it with data sets of images with pedestrians and measuring the percent correctly identified by the system. This training allows the system to evaluate the data and learn from its right or wrong answers. However, scientists must be careful to use a variety of data is used otherwise unintentional bias can occur, potentially skewing the data and impairing the system’s ability to make real-world decisions.

Types of Machine Learning

There are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

Supervised Learning

During supervised learning, the system must first undergo a training phase where it will be provided with both examples and their labels to properly identify them. Then, the system will receive a new data set of the same examples and its algorithm will now be able to identify these examples based on correlations it found from the training data it received previously.

Unsupervised Learning

During unsupervised learning, a system is provided with unlabeled data and its algorithm must analyze it to find patterns. One way this could be utilized is by forming clusters of information which are grouped by certain common qualities.

Reinforcement Learning

During reinforcement learning, the system receives an unlabeled dataset and is required to form a conclusion immediately. If the conclusion is incorrect, the scientist provides the system with feedback and the algorithm uses it to come up with a new conclusion. This process continues until the system produces a correct answer, which is also known as a reinforced response. Essentially, the main goal of reinforcement learning is to let the system learn from its own mistakes to ultimately provide the correct solution.

Impact of Machine Learning on the World

Despite having different forms of machine learning, the ultimate purpose is to train a computing system to perform in an intelligent and responsive manner, similar to that of a human being. It is clear the machine learning poses a great advantage for developing and advanced technologies. But what impact does it have on business or productivity? Today’s world is highly dependent on data, and it is imperative for businesses to draw conclusions from data to make changes and gain advantages. Instead of sorting through data by hand and wasting both time and capital, computing devices can find patterns from data and draw appropriate conclusions through machine learning. This improves efficiency, time management, and ensures that all types of data can be handled since machine learning is not limited by amount of category.

I believe that with the rise our big data world, machine learning will slowly become more and more integrated in our lives to the point where the phrase will become a basic household name rather than a science fiction reach. J

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