Introduction:
Machine learning has great potential to change the way companies work and transform them into something that is more recognized. And in reality, it’s quite mundane than robot drivers and chefs. What we’re trying to explain here is that machine learning has a great impact on organizations which makes others think this concept is difficult to understand which is so not true. In fact, think of it as a simple branch of statistics that has been designed for the world of big data. It is because of machine learning that you get to hear about a new algorithm that can drive a car, or scan a picture and find your own face in a crowd. It seems every week or a month later, companies are able to find new uses for algorithms that they adapt while encountering new data.
So by evaluating how important it is to understand what machine learning is and how helpful it can turn out to be for your organization, executives should understand what it is, what it is capable of doing. Similarly, what to watch out for when using it and whatnot.
Importance of machine learning:
To help you understand the importance of machine learning, we will be explaining different facts which are mentioned below.
Firstly, when you’re a part of a big organization then it is obvious that you are going to have to deal with the enormous scale of data. But this can pose several challenges to the firm which can be difficult to handle. Now one may say that for big data we have advanced software and hardware that can handle and store the data. You’re thinking right here, but did you ever wonder that big data is not just long but is quite wide. And to deal with data that is wide as well, machine learning has such tools that have been designed to make better use of the wide data as well.
Secondly, machine learning tools are widely used to make predictions. Now what kind of predictions it can make right? Here are a few examples of prediction problems that can occur in a business.
- Machine learning tools can forecast long-term customer loyalty.
- It is used to make personalized recommendations for customers.
- Similarly, the future performance of employees can be anticipated using machine learning tools easily.
- Moreover, the credit risk of loan applicants is rated using machine learning tools.
With that, there are also complex situations that we might encounter where the right decision depends on a variety of variables. Here wide data is required to make the right decision. Online platforms are providing many online free courses like NPTEL Machine Learning Courses are an amazing opportunity to learn ML. Similarly, there also comes a situation where you have to make a very important business decision that is going to require an accurate prediction. Thus in these mentioned and many other cases, machine learning tools are used widely.
Thirdly, with machine learning, you’re not focused on causality. This means that you don’t have to worry about what is going to happen when you change the environment. Instead, you just have to focus on the prediction that is, you only need a model of the environment so that you can make the right decision. To explain this more clearly, let us take a simple example where you have to decide whether to take an umbrella while leaving the house or not. Now to make this decision, you have to predict before deciding whether to bring the umbrella or not right? Even though you can use the weather forecast and is very helpful yet it is limited and will not tell you how the clouds and even the umbrella works or more. So when you realize that there is these type of limitations that you have to face, the importance of machine learning becomes even more obvious to you.
How is Machine Learning used in practice?
Up till now, we only talked about the importance of machine learning like what it does and how it plays an important part in any organization. But how it is used in practice is also something worth explaining. To talk very precisely, we’d say that the machine learning algorithm works on three broad concepts which are feature extraction, regularization, and cross-validation. Understanding these three concepts would help you put machine learning in practice easily.
Feature extraction:
The feature extraction is used to find out the type of data that we are going to use in a particular model. In other words, the feature extraction enables us to decide what type of variables we will be using in our model. And sometimes by that, we also mean to dump all the available raw data straight in and use it in the model.
Regularization:
Similarly, regularization determines how the data is going to be weighted within the model. This means that through regularization, we are able to find out if the features that we have extracted actually reflect signal rather than noise. The main goal here is to play safe and not jump to any conclusions about our model. That is why regularization enables us to make sure our goal is achieved.
Cross-validation:
Lastly, cross-validation is used to test the accuracy of the model. After you’re done making a model, how sure can you be that it is going to make accurate or good predictions? That is where cross-validation comes in which will help you find whether the model is accurate and is making predictions for data it has never seen before. Though it can be costly yet this concept is really important in machine learning.
Conclusion:
Using these three concepts will help us sort through this mixture of signal and noise in order to make better predictions. Thus we can say that machine learning is indeed helping organizations inventing new things after every short interval. With the right mix of human judgment and technical skills, machine learning can be a very useful tool for making decisions and making sense of the inherent problems of the wide data that we deal with every day. So, understand what machine learning is and try to make the most out of it and never stop learning.
