What is Machine Learning?
Machine learning is a part of artificial intelligence that provides systems with the ability to automatically learn and improve from experiences. The aim is to allow computers to make decisions that are completely data-driven.
The ML model is unlike any traditional software where a human developer incorporates a written code that performs the next action. Instead, an ML model has been taught how to draw inferences from the given input data.
How Machine Learning Works
The machine learning model uses two types of techniques, supervised learning and unsupervised learning. In the former, the data scientist acts as a guide for the machine, where it is trained with a known input and output data so that it can predict future outputs. In the latter, a future output is predicted based on hidden patterns in the input data.
Here’s an example that explains the functioning of ML:
Let’s say you want to buy the best phone in the market. You would probably search the Internet to find a phone with the maximum number of good reviews and will purchase the one with maximum reviews.
Therefore, the reviews from the previous users who purchased the phone helped the future users to make a buying decision. This resembles a pattern amongst the earlier users who purchased the phone and the future users who are about to complete a purchase. So machine learning is typically to understand any hidden pattern and draw inferences from the input data.
What is the importance of machine learning?
ML is enhancing business scalability and improving business operations for companies across the globe. In literal terms, ML pulls out meaningful insights from raw data to quickly solve data-rich business problems. It is everywhere, from self-driving cars to face recognition on Facebook.
Some statistics showing the prominence of machine learning:
76% of business entrepreneurs experienced an increase in sales after implementing machine learning. Not only this, nearly 60% of businesses are keen to apply Machine learning to enhance profits by marketing.
What is machine learning used for?
With Machine Learning gaining momentum, you should know when to use machine learning and when not to use it. Solving a complex problem/task involving a large amount of data and lots of variables is when you should be using machine learning.
Machine learning should not be used in cases where the machine cannot detect patterns. It should also not be used in instances where rules change constantly like in fraud detection or predicting shopping trends.
Advantages of Machine Learning
Sales and marketing- Machine learning helps studies the customer behaviour patterns from previous marketing campaigns. For example, machine learning will automatically optimize a relevant advertisement that perfectly fits your customer’s needs. This helps businesses make accurate sales prediction.
Products recommendation- Every person is different and so are their preference for clothes, songs, movies etc. Organizations like Amazon, Netflix uses input data to draw inferences that predict what a user might be interested in.
Finance sector- Enabling an ML model in the finance sector helps in preventing frauds. It continuously assesses the data and detects any anomalies and nuances.
Healthcare- In this sector, enabling the ML model helps in identifying high-risk patients. This helps it to identify nearly accurate diagnosis and recommending the best possible medicines.
Disadvantages of machine learning
Usage is limited- Even after the ML model is able to solve various complex problems, however, we are unsure that ML algorithms will work in every case.
Needs earlier records of data- There is a lesser possibility that a machine learning system can make immediate predictions because it basically reads the earlier input data.
We hope you have a better understanding of the basics of machine learning and how it works. If we missed out on some points, let us know in the comments below! What are your thoughts on machine learning? Does your business currently make use of machine learning?
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