9 Real-World Problems that can be Solved by Machine Learning
Machine Learning has gained a lot of prominence in the recent years because of its ability to be applied across scores of industries to solve complex problems effectively and quickly. Contrary to what one might expect, Machine Learning use cases are not that difficult to come across. The most common examples of problems solved by machine learning are image tagging by Facebook and spam detection by email providers.
AI for business can resolve incredible challenges across industry domains by working with suitable datasets. In this post, we will learn about some typical problems solved by machine learning and how they enable businesses to leverage their data accurately.
What is Machine Learning?
A sub-area of artificial intelligence, machine learning, is an IT system's ability to recognize patterns in large databases to find solutions to problems without human intervention. It is an umbrella term for various techniques and tools to help computers learn and adapt independently.
Unlike traditional programming, a manually created program that uses input data and runs on a computer to produce the output, in Machine Learning or augmented analytics, the input data and output are given to an algorithm to create a program. It leads to powerful insights that can be used to predict future outcomes.
Machine learning algorithms do all that and more, using statistics to find patterns in vast amounts of data that encompass everything from images, numbers, words, etc. If the data can be stored digitally, it can be fed into a machine-learning algorithm to solve specific problems.
Types Of Machine Learning
Today, Machine Learning algorithms are primarily trained using three essential methods. These are categorized as three types of machine learning, as discussed below –
1. Supervised Learning
One of the most elementary types of machine learning, supervised learning, is one where data is labeled to inform the machine about the exact patterns it should look for. Although the data needs to be labeled accurately for this method to work, supervised learning is compelling and provides excellent results when used in the right circumstances.
For instance, when we press play on a Netflix show, we generate a Machine Learning problem statement to find similar shows based on our preferences.
How it works –
- The Machine Learning algorithm here is provided with a small training dataset to work with, which is a smaller part of the bigger dataset.
- It serves to give the algorithm an idea of the real-time problem statement, solution, and various data points to be dealt with.
- The training dataset here is also very similar to the final dataset in its characteristics and offers the algorithm with the labeled parameters required for the problem.
- The Machine Learning algorithm then finds relationships between the given parameters, establishing a cause and effect relationship between the variables in the dataset.
2. Unsupervised Learning
Unsupervised learning, as the name suggests, has no data labels. The machine looks for patterns randomly. It means that there is no human labor required to make the dataset machine-readable. It allows much larger datasets to be worked on by the program. Compared to supervised learning, unsupervised Machine Learning services aren’t much popular because of lesser applications in day-to-day life.
How does it work?
- Since unsupervised learning does not have any labels to work off, it creates hidden structures.
- Relationships between data points are then perceived by the algorithm randomly or abstractly, with absolutely no input required from human beings.
- Instead of a specific, defined, and real-time problem statement, unsupervised learning algorithms can dynamically adapt to the data by changing hidden structures.
3. Reinforcement Learning
Reinforcement learning primarily describes a class of machine learning problems where an agent operates in an environment with no fixed training dataset. The agent must know how to work using feedback.
How does it work?
- Reinforcement learning features a machine learning algorithm that improves upon itself.
- It typically learns by trial and error to achieve a clear objective.
- In this Machine Learning algorithm, favorable outputs are reinforced or encouraged, whereas non-favorable outputs are discouraged.
9 Real-World Problems Solved by Machine Learning
Applications of Machine learning are many, including external (client-centric) applications such as product recommendation, customer service, and demand forecasts, and internally to help businesses improve products or speed up manual and time-consuming processes.
Machine learning algorithms are typically used in areas where the solution requires continuous improvement post-deployment. Adaptable machine learning solutions are incredibly dynamic and are adopted by companies across verticals.
Here we are discussing nine Machine Learning use cases –
1. Identifying Spam
Spam identification is one of the most basic applications of machine learning. Most of our email inboxes also have an unsolicited, bulk, or spam inbox, where our email provider automatically filters unwanted spam emails.
But how do they know that the email is spam?
They use a trained Machine Learning model to identify all the spam emails based on common characteristics such as the email, subject, and sender content.
If you look at your email inbox carefully, you will realize that it is not very hard to pick out spam emails because they look very different from real emails. Machine learning techniques used nowadays can automatically filter these spam emails in a very successful way.
Spam detection is one of the best and most common problems solved by Machine Learning. Neural networks employ content-based filtering to classify unwanted emails as spam. These neural networks are quite similar to the brain, with the ability to identify spam emails and messages.
2. Making Product Recommendations
Recommender systems are one of the most characteristic and ubiquitous machine learning use cases in day-to-day life. These systems are used everywhere by search engines, e-commerce websites (Amazon), entertainment platforms (Google Play, Netflix), and multiple web & mobile apps.
Prominent online retailers like Amazon and eBay often show a list of recommended products individually for each of their consumers. These recommendations are typically based on behavioral data and parameters such as previous purchases, item views, page views, clicks, form fill-ins, purchases, item details (price, category), and contextual data (location, language, device), and browsing history.
These recommender systems allow businesses to drive more traffic, increase customer engagement, reduce churn rate, deliver relevant content and boost profits. All such recommended products are based on a machine learning model’s analysis of customer’s behavioral data. It is an excellent way for online retailers to offer extra value and enjoy various upselling opportunities using machine learning.
3. Customer Segmentation
Customer segmentation, churn prediction and customer lifetime value (LTV) prediction are the main challenges faced by any marketer. Businesses have a huge amount of marketing relevant data from various sources such as email campaigns, website visitors and lead data.
Using data mining and machine learning, an accurate prediction for individual marketing offers and incentives can be achieved. Using ML, savvy marketers can eliminate guesswork involved in data-driven marketing.
For example, given the pattern of behavior by a user during a trial period and the past behaviors of all users, identifying chances of conversion to paid version can be predicted. A model of this decision problem would allow a program to trigger customer interventions to persuade the customer to convert early or better engage in the trial.
4. Image & Video Recognition
Advances in deep learning problem statements and algorithms have stimulated rapid progress in image & video recognition techniques over the past few years. They are used for multiple areas, including object detection, face recognition, text detection, visual search, logo and landmark detection, and image composition.
Since machines are good at processing images, Machine Learning algorithms can train Deep Learning frameworks to recognize and classify images in the dataset with much more accuracy than humans.
Similar to image recognition, companies such as Shutterstock, eBay, Salesforce, Amazon, and Facebook use Machine Learning for video recognition where videos are broken down frame by frame and classified as individual digital images.
5. Fraudulent Transactions
Fraudulent banking transactions are quite a common occurrence today. However, it is not feasible (in terms of cost involved and efficiency) to investigate every transaction for fraud, translating to a poor customer service experience.
Machine Learning in finance can automatically build super-accurate predictive maintenance models to identify and prioritize all kinds of possible fraudulent activities. Businesses can then create a data-based queue and investigate the high priority incidents.
It allows you to deploy resources in an area where you will see the greatest return on your investigative investment. Further, it also helps you optimize customer satisfaction by protecting their accounts and not challenging valid transactions. Such fraud detection using machine learning can help banks and financial organizations save money on disputes/chargebacks as one can train Machine Learning models to flag transactions that appear fraudulent based on specific characteristics.
6. Demand Forecasting
The concept of demand forecasting is used in multiple industries, from retail and e-commerce to manufacturing and transportation. It feeds historical data to Machine Learning algorithms and models to predict the number of products, services, power, and more.
It allows businesses to efficiently collect and process data from the entire supply chain, reducing overheads and increasing efficiency.
ML-powered demand forecasting is very accurate, rapid, and transparent. Businesses can generate meaningful insights from a constant stream of supply/demand data and adapt to changes accordingly.
7. Virtual Personal Assistant
From Alexa and Google Assistant to Cortana and Siri, we have multiple virtual personal assistants to find accurate information using our voice instruction, such as calling someone, opening an email, scheduling an appointment, and more.
These virtual assistants use Machine Learning algorithms for recording our voice instructions, sending them over the server to a cloud, followed by decoding them using Machine Learning algorithms and acting accordingly.
8. Sentiment Analysis
Sentiment analysis is one of the beneficial and real-time machine learning applications that help determine the emotion or opinion of the speaker or the writer.
For instance, if you’ve written a review, email, or any other form of a document, a sentiment analyzer will be able to assess the actual thought and tone of the text. This sentiment analysis application can be used to analyze decision-making applications, review-based websites, and more.
9. Customer Service Automation
Managing an increasing number of online customer interactions has become a pain point for most businesses. It is because they simply don’t have the customer support staff available to deal with the sheer number of inquiries they receive daily.
Machine learning algorithms have made it possible and super easy for chatbots and other similar automated systems to fill this gap. This application of machine learning enables companies to automate routine and low priority tasks, freeing up their employees to manage more high-level customer service tasks.
Further, Machine Learning technology can access the data, interpret behaviors and recognize the patterns easily. This could also be used for customer support systems that can work identical to a real human being and solve all of the customers’ unique queries. The Machine Learning models behind these voice assistants are trained on human languages and variations in the human voice because it has to efficiently translate the voice to words and then make an on-topic and intelligent response.
If implemented the right way, problems solved by machine learning can streamline the entire process of customer issue resolution and offer much-needed assistance along with enhanced customer satisfaction.
Top 4 Issues with Implementing Machine Learning
While Machine learning is extensively used across industries to make data-driven decisions, its implementation observes many problems that must be addressed. Here’s a list of organizations' most common machine learning challenges when inculcating ML in their operations.
1. Inadequate Training Data
Data plays a critical role in the training and processing of machine learning algorithms. Many data scientists attest that insufficient, inconsistent, and unclean data can considerably hamper the efficacy of ML algorithms.
2. Underfitting of Training Data
This anomaly occurs when data fails to link the input and output variables explicitly. In simpler terms, it means trying to fit in an undersized t-shirt. It indicates that data isn’t too coherent to forge a precise relationship.
3. Overfitting of Training Data
Overfitting denotes an ML model trained with enormous amounts of data that negatively affects performance. It's similar to trying an oversized jeans.
4. Delayed Implementation
ML models offer efficient results but consume a lot of time due to data overload, slow programs, and excessive requirements. Additionally, they demand timely monitoring and maintenance to deliver the best output.
Wrapping Up
As advancements in machine learning evolve, the range of use cases and applications of machine learning too will expand. To effectively navigate the business issues in this new decade, it’s worth keeping an eye on how machine learning applications can be deployed across business domains to reduce costs, improve efficiency and deliver better user experiences.
However, to implement machine learning accurately in your organization, it is imperative to have a trustworthy partner with deep-domain expertise. At Maruti Techlabs, we offer advanced machine learning services that involve understanding the complexity of varied business issues, identifying the existing gaps, and offering efficient and effective tech solutions to manage these challenges.
If you wish to learn more about how machine learning solutions can increase productivity and automate business processes for your business, get in touch with us.
FAQs
1. What are the problems solved by machine learning?
The following types of problems are typically solved by machine learning:
- Identifying Spam: Filters spam emails automatically.
- Product Recommendations: Suggests products based on customer behavior.
- Customer Segmentation: Groups customers for targeted marketing.
- Image & Video Recognition: Recognizes and classifies images and videos.
- Fraud Detection: Identifies fraudulent transactions.
- Demand Forecasting: Predicts product demand.
- Virtual Assistants: Powers tools like Alexa and Siri.
- Sentiment Analysis: Analyzes emotions in text.
- Customer Service Automation: Automates routine inquiries.
2. What are machine learning problem statements?
Now that you know the various real-world problems machine learning can solve, if you have your project requirements ready, you can start creating your problem statements to help your development team better understand what you aim to achieve - just as you make business problem statements. Here is an example of a healthcare machine learning problem statement - Develop a machine learning model to predict patient readmissions within 30 days of discharge from the hospital. The model should analyze patient records, including demographics, medical history, treatment received, and post-discharge care.
