What is Machine Learning?
We’ve all shopped online, right? While checking on products, have you noticed how suddenly all of your recommendations are now centered around what you were searching for? Or the famous “frequently bought together” suggestions. Is it a common coincidence? No, it’s machine learning.
Do you ever get phone calls from insurance agencies, banks, or loan companies trying to get your business? They don’t just call everyone in your city. They only call very specific people because they think you will be a customer. This is a calculated and specific move gauged by target marketing. Machine learning allows for this easy process. Can you imagine how long this would take to create a call list without machine learning?
So let’s talk about what Machine Learning actually is. Officially, Machine Learning is defined as a subset of artificial intelligence that focuses on machine learning based on predictions based on experience. This allows for our devices (computers, machines, data-driven applications) to make thoughtful decision based on facts and experience opposed to making random selections without a theme. Essentially, machine learning allows for a device to carry out a certain task with little instruction. Over time through learning more patterns, data, and predictions these algorithms and these processes improve.
The Growth of Machine Learning
In recent years we are living in an environment where humans and machines work side by side. Like humans, machines also evolve, learn, and apply what they know to make better, more thoughtful decisions. We are only in the beginning stages of machine evolutions which means there is much more to learn. However, for being in the primitive age we are already progressing beyond what we even imagined.
How Does it Work?
Training data sets are used to create a model from Machine Learning algorithms. Then when new data is introduced into the model, it is able to estimate or predict a next move based upon that algorithm. The estimation or prediction is then evaluated based upon its relevance and accuracy. A Machine Learning algorithm is then created if accepted. If it is not determined to be valid or acceptable, it will train over and over with new data sets until the system is satisfied with the predictions.
Although there are many factors and steps involved in this complex system, this is a simplified version.
Supervised Learning.
Supervised Learning is the type of Machine Learning where you can consider the learning as guided by a teacher. We have a data set which acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a prediction or decision when new data is given to it.
The model learns through observation and finds structures in the data. Once the model is given a data set, it automatically finds patterns and relationships in the data set to match the patterns and structures it learned.
Real Life Examples of Machine Learning.
Image recognition is one of the most common uses of machine learning. There are many situations where you can classify the object as a digital image. For example, in the case of a black and white image, the intensity of each pixel serves as one of the measurements.
Speech recognition is the translation of spoken words into text. It is also known as computer speech recognition or automatic speech recognition. Here, a software application can recognize the words spoken in an audio clip or file, and then subsequently convert the audio into a text file.
Financial Services: Machine learning has a lot of potential in the financial and banking sector. It is the driving force behind the popularity of financial services. Machine learning can help the banks and financial institutions to make smarter decisions. Machine learning can help financial services to spot an account closure before it occurs.
Prediction: Machine learning can also be used in prediction systems. Consider a loan example; to compute the probability of a fault, the system will need to classify the available data in groups. It is defined by a set of rules prescribed by the analysts. Once the classification is done, we can calculate the probability of the fault.
Learning associations is the process of developing insights into the various associations between the products. A good example is how unrelated products can be associated with one another. One of the applications of machine learning is studying the associations between the products that people buy. If a person buys a product, he will be shown similar products because there is a relation between the two products.
Machine Learning is on the rise! Stay tuned for more ML and AI blogs.
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