In this decade, Machine learning is one of the most exciting fields in the tech world. And it’ll only keep getting higher. According to the latest research, investment in AI and machine-learning startups will reach $12 billion this year and continue climbing past that point. What’s more, we already see an increase in demand for roles related to ML. Not just for data scientists and programmers who can implement ML algorithms but also for people with skills across HR, sales, marketing, and other non-technical roles who can combine their job with these new technologies. Whether you want to become a data scientist or want to explore the possibilities of machine learning at work, here we will look at everything you need to know about it.
What is Machine Learning?
Machine learning, often abbreviated as ML, is a field of computer science that teaches machines to learn without being programmed. In practice, a software program trains or learn with existing data and then make predictions about new data with their previous learning. The idea is that, as time goes on, the program will deliver more accurate results (the Predictions) as it “learns” from its mistakes and applies that knowledge to new data. The simplest way to put it is that you feed a computer a bunch of data and a goal (e.g., predict whether a patient will survive five years after diagnosis based on a set of data), and the computer figures out how to achieve that goal.
How Does Machine Learning Work?
The best way to understand how machine learning works is to think about a toddler learning to walk. The first time, the baby stumbles and falls all the time because it doesn’t know how to control its body on its own. But each time it goes down, it picks up itself and tries again. Eventually, the baby figures out how to walk and doesn’t need to think about moving its legs or balancing its weight. The same concept applies to machine learning. When software engineers build a machine learning program, they provide it with a set of instructions called a model. This model tells the system what kind of data to use, what the desired and expected outcome of that data is, and what parts of data are most important to achieving that outcome. When the program processes a new data set and makes a prediction, it’s natural to get it wrong. At that time, The engineer can check what factors influenced the wrong prediction and then adjust the model.