Machine Learning use case:
Friends have you ever thought of that why we need this machine learning stuff in our life.🧐Let’s see 🧐
Let’s consider we have a dataset means a collection of data of particular domain e.g marks secured by the students in last sem along with their names and hours of study.
We gather data with some goal in our mind . This collection of data is known as data gathering
The goal can be if in future we require this data then we can provide it easily to anyone. If some clg teacher is interested to see the data regarding the performance of their students then so for this they need to perform some computations and find either total sum of marks or the average.
So here goal of data collection is to find the performance of students based on total marks or average.
This is known as data analysis , where we collect the data and perform some calculations to fulfill our requirements.
Let’s say student named Rohit came to clg and says that he studied for 5 hrs for the current exam and so he was thinking about how much marks did he really get??
Can you all predict it if you have the previous all sems results of Rohit ? Atleast you can predict the approximate marks not always exact . Correct??
Yes, it’s very much for sure that we can predict his marks based on the previous data of last sem which has marks and also the hrs studied in total. So how we had done this?? By gaining experience , by getting trained by the previous data, by learning exactly the correlation between the total hours studied and total marks obtained by particular student. Correct?? Yes, that’s the excat way we have done.
But why humans predict these things always , we can invest our time in some other fruitful work and it’s impossible for us to predict some complex and bigdata. So what’s the solution for this?? Let’s give this work to machines.
Yaa you heard it correct give this prediction work to our computers.
But computers doesn’t have the human mind so how come it can predict that?🤔🤔
Machine Learning comes in use here.
ML has multiple algorithms that helps us in predicting the future values.
So let’s see how ML benefits us in various usecases:
1. Voice assistants:
This consumer-based use for machine learning applies mostly to smart phones and smart home devices. The voice assistants on these devices use machine learning to understand what you say and craft a response. The machine learning models behind voice assistants were trained on human languages and variations in the human voice, because it has to translate what it hears into words and then make an intelligent, on-topic response.
Millions of consumers use this technology, often without realizing the complexity behind the tool. The concept of training machine learning models to follow rules is fairly simple, but when you consider training a model to understand the human voice, interpret meaning, and craft a response, that is a heavy task.
2. Dynamic pricing:
This machine–based pricing strategy is most known in the travel industry. Flights, hotels, and other travel bookings usually have a dynamic pricing strategy behind them. Consumers know that the sooner they book their trip the better, but they may not realize that the actual price changes are made via machine learning.
Travel companies set rules for how much the price should increase as the travel date gets closer, how much it should increase as seat availability decreases, and how high it should be relative to competitors. Then, they let the machine learning model run with competitor prices, time, and availability data feeding into it.
3. Email filtering:
This is a classic use of machine learning. Email inboxes also have a spam inbox, where your email provider automatically filters unwanted spam emails. But how do they know when an email is spam? They have trained a model to identify spam emails based on characteristics they have in common. This includes the content of the email itself, the subject, and the sender. If you’ve ever looked at your spam inbox, you know that it wouldn’t be very hard to pick out spam emails because they look very different from real emails.
Amazon and other online retailers often list “recommended products” for each consumer individually. These recommendations are based on past purchases, browsing history, and any other behavioral information they have about consumers. Often the recommendations are helpful in finding related items that you need to complement your purchase (think batteries for a new electronic gadget).
However, most consumers probably don’t realize that their recommended products are a machine learning model’s analysis of their behavioral data. This is a great way for online retailers to provide extra value or upsells to their customers using machine learning.
5. Personalized marketing:
Marketing is becoming more personal as technologies like machine learning gain more ground in the enterprise. Now that much of marketing is online, marketers can use characteristic and behavioral data to segment the market. Digital ad platforms allow marketers to choose characteristics of the audience they want to market to, but many of these platforms take it a step further and continuously optimize the audience based on who clicks and/or converts on the ads. The marketer may have listed 4 attributes they want their audience to have, but the platform may find 5 other attributes that make users more likely to respond to the ads.
6. Process automation:
There are many processes in the enterprise that are much more efficient when done using machine learning. These include analyses such as risk assessments, demand forecasting, customer churn prediction, and others. These processes require a lot of time (possibly months) to do manually, but the insights gained are crucial for business intelligence. But if it takes months to get insights from the data, the insights may already be outdated by the time they are acted upon. Machine learning for process automation alleviates the timeliness issue for enterprises.
Industries are getting more and more competitive now that technology has sped up these processes. Companies can get up-to-date analyses on their competition in real time. This high level of competition makes customer loyalty even more crucial, and machine learning can even help with customer loyalty analyses like sentiment analysis. Companies like Weavr.ai provide a suite of ML tools to enable this type of analysis quickly and deliver results in a consumable format.
7. Fraud detection:
Banks use machine learning for fraud detection to keep their consumers safe, but this can also be valuable to companies that handle credit card transactions. Fraud detection can save money on disputes and chargebacks, and machine learning models can be trained to flag transactions that appear fraudulent based on certain characteristics.
Machine learning can provide value to consumers as well as to enterprises. An enterprise can gain insights into its competitive landscape and customer loyalty and forecast sales or demand in real time with machine learning.
So , these are some of the use cases of ML in the real world , other multiple usecases are also come under ML.
Hope you all loved this blog.
Thanks in advance for reading 💯