Editor’s Note: The following guest post comes from Nirmal Patel, Nirmal is going to share with us some basic concepts of Machine Learning for beginners.
Though it has been around for a while, there is a new and increased interest in Machine Learning over the past few years with advancements in the field. Increased application of Machine Learning in AI has led to an increased hype but also an increased confusion. The ideal place to begin unknotting these complex concepts is probably an online beginners data science course. But read ahead to understand some of the basic concepts involved.
To put it simply, Machine Learning is a machine that has been designed to use the data provided and learn to do a specific task from the data. It helps the device increase its efficiency to perform a given function. As opposed to another coding where humans have to specify every task and objective, Machine Learning accesses the given data and learns a way to perform the job by itself. Machine Learning is often confused with related concepts like Data Mining, Artificial Intelligence, Deep Learning, Neural Network and more. However, it is important to note that these concepts are all different from Machine Learning.
To accomplish this, there first has to be enormous amounts of data collected. Machine Learning heavily relies on data to develop. So this is an essential step. The more the data, the better can the machine learn. But, the better the data, the more successful will the result be. Which is why, after collecting the data, it needs to be sorted and cleaned.
Depending on the function of the Machine Learning model, an appropriate algorithm needs to be chosen. The data is represented to train the machine to learn. Think of it like getting a baby to understand what a rose is. You show the baby roses of all different shapes, sizes, colors and more and then try to get the baby to identify a rose. So some data is used to train the machine, and some information is used to test its success.
As mentioned earlier, the algorithm used for the model depends on the function of the model. There are three types of Machine Learning algorithms – Supervised Learning, Unsupervised Learning and Reinforcement Learning.
- Supervised learning is also called predictive learning, and as the name suggests, it is used for prediction. You feed it as much relevant data as possible and using this data, and the machine will predict future outcomes. Most of the machine learning models use supervised learning. For example, supervised learning can be used to predict how much it would cost you to get from one place to another.
- Unsupervised Learning is where you only have input data, but there is no single correct answer when it comes to the output. It is mainly used for identifying inherent features and structures within the data. This can be used for marketing purposes by helping a company study consumer behavior and patterns.
- Reinforcement Learning is where the machine uses data along with incoming data from the environment and to teach itself. Here the model not only relies on the data provided but it also continuously learns from the increased exposure it gets. It works like a brain but without any human intervention. Reinforcement learning can be used to make more complex decisions about businesses.
Giants like Google and Facebook have been using Machine Learning for quite some time to improve the relevance of searches and tagging friends in photos. With recent advancements in the field and with improved hardware, there has been a surge in Machine Learning training across the world. You can use such training and data science courses to add to your understanding of these basic concepts in Machine Learning!