10 Real-World Problems Solved by Machine Learning

Machine Learning has acquired particular fame in recent years because of its capability to be implemented across several sectors to address intricate issues efficiently and competently. Despite of what one may think, machine learning applications are easy to come by. Common implications of machine learning involve tasks like the categorization of images by platforms like Facebook  and the identification of spam by email services

AI and machine learning use cases can handle implausible issues across industry areas by working with suitable datasets. In this post, we will discover some typical real-world machine learning examples and the ways they enable enterprises to use their data correctly.

What Is Machine Learning?

Machine learning, a branch of artificial intelligence, basically refers to the ability of IT systems to identify patterns in huge data sets and solve issues without the requirement of human involvement. It is an umbrella term including several techniques and tools aimed at allowing computers to learn and adapt on their own.

In contrast to traditional programming, in which a program is physically written to process input data and then produce results, augmented analytics or Machine learning includes supplying input data and results to an algorithm, which then creates the program. This method assists uncover valuable insights that can be utilized to predict future outcomes.

Machine learning algorithms use statistical techniques to unveil patterns in huge datasets, ranging from images and numbers to text, extending simple analysis. If the information can be digitized, it can be put into a machine-learning algorithm to solve particular issues. Such algorithms are important in addressing a range of real-world problems that can be solved with machine learning and have several AI and machine learning use cases across sectors.

Types of Machine Learning

At present, there are three main methods used to train machine learning algorithms. Such methods can be classified into three kinds of machine learning, as described below:

1. Supervised Learning

One of the most fundamental types of machine learning, supervised learning includes using labeled data to teach the machine precisely which patterns to analyze. However correct labeling of information is important for this method,  supervised learning is highly effective and produces excellent outcomes when implemented in the correct scenarios.

For instance, when we start watching a show on Netflix, it activates a machine-learning process that recommends similar shows based on our viewing preferences.

How it works:

In supervised learning, the Machine Learning algorithm is offered with a small training dataset, which is basically a subset of the larger dataset. This assists the algorithm in understanding the real-time problem statement and the ways to tackle many data points.

The training dataset reflects the final dataset in its characteristics and offers labeled parameters needed for solving the issue. The algorithm then analyzes relationships between these factors, developing cause-and-effect links among the variables in the dataset.

2. Unsupervised Learning

As the name says, unsupervised learning includes no labeled data. The machine finds for patterns without particular guidance. This depicts no human intervention is required to make the dataset machine-readable, permitting the program to work with huge datasets. Although, as compared to supervised learning, unsupervised Machine Learning has less real-world machine learning examples in routine interventions.

How it works:

Since there are no labels to help the algorithm, it develops hidden structures in the data. The algorithm analyzes relationships between points of data in an abstract or random way, with no input from humans. Instead of focusing on a particular, defined problem statement for machine learning project, algorithms of unsupervised learning actively adapt to the information, regulating the hidden structures as required.

3. Reinforcement Learning

It is a kind of Machine Learning in which an agent communicates with an environment without any fixed training dataset. Rather than this, the agent learns how to conduct tasks by means of feedback.

How it works:

In reinforcement learning, the machine learning algorithm enhances by means of trial and error to reach a particular objective. Positive results are encouraged, while negative results are penalized. This iterative procedure allows the algorithm to enhance over time, providing solutions to intricate real-world issues that can be solved through machine learning, and creating valuable problem statements across many areas.

10 Real-Life Challenges Addressed by Machine Learning

Machine learning applications span a huge range of areas, involving external (client-focused) apps such as product suggestions, customer service, and required predictions, as well as internal utilizations that assist businesses to improve products or automate manual, time-taking tasks.

Machine learning algorithms are usually used in situations that need persistent enhancements after deployment. Such adaptable solutions are active, allowing enterprises across many sectors to use AI and machine learning use cases efficiently.

Real-World Problems Cracked by Machine Learning

Below are 10 real-world machine learning examples where Machine learning offers impactful solutions:

1. Identifying Spam

Detection of spam is one of the most important machine learning applications. Most of us depend on email providers to automatically filter all the spam, but how do such systems know that all emails are spam? They use a trained machine model to analyze patterns in the email body, subject line, and information of the sender.

This system uses machine learning problem statements to automatically flag unwanted emails. Neural networks, which are similar to the brain, use content-based filtering to categorize emails as spam. This is a main real-world problem that can be solved with machine learning.

2. Making Product Suggestions

Recommender systems are a common machine learning application in routine life, employed by search engines, entertainment apps (Netflix, Google Play), and e-commerce platforms (Amazon). Such systems offer customized product recommendations based on older purchases, behavioral data, and page views.

These systems not only enhance customer engagement but also create sales by providing related content custom to particular preferences, hence depicting the influence of how machine learning solves problems in business.

3. Customer Segmentation

Customer segmentation, customer lifetime value (LTV) prediction, and churn prediction are the main issues for marketers. Machine learning in healthcare and other segments can assist enterprises use huge datasets to custom marketing efforts more accurately, reducing guesswork.

For example, by identifying user behavior during a period of trial, organizations can forecast the chances of conversion to a paid service. This permits enterprises to boost their techniques through problem statements for machine learning projects that create targeted interventions.

4. Image & Video Recognition

Progressions in deep learning and machine learning problem statements have caused fast development in video and image recognition. Organizations use such technologies for tasks like face recognition, object detection, and visual search.

Machine learning algorithms can categorize images with far greater correctness than humans, helping platforms such as Facebook and eBay in video recognition by breaking down the videos into particular frames. This ability exemplifies the power of real-life problem statements for projects in several areas.

5. Fraudulent Transactions

Fraud detection is one more important machine learning application, specifically in finance and banking. Machine learning in the healthcare and financial sectors can aid in analyzing fraudulent activities by identifying transaction patterns in reality.

By developing predictive models, financial institutions can mark doubtful transactions for review without the requirement to physically examine every single one.

6. Demand Predictions

Demand forecasting is important across industries such as manufacturing, retail, and transportation. By feeding historical information into machine learning algorithms, enterprises can forecast requirements for services and products, enhance their supply chains, and minimize overheads.

This kind of real-world machine learning example depicts the capability of AI and machine learning use cases to enhance operational competency and adapt rapidly to market modifications.

7. Virtual Personal Assistant

Virtual assistants such as Google Assistant, Alexa, Cortana, and Siri use machine learning applications to understand and react to voice commands. Such systems record voice input, send it to the cloud and also decode it using machine learning algorithms to conduct several tasks.

Such assistants are an outstanding instance of how machine learning solves problems related to human-computer communication, using natural language processing to enhance the experience of the user.

8. Sentiment Analysis

It is a valuable application of machine learning that decides the emotional tone of the written content, like social media posts or reviews. By identifying text data, machine learning models can identify opinions, emotions, and sentiments, which can be utilized in the process of decision-making or any customer feedback analysis.

This is a perfect example of real-world problems that can be solved with machine learning, allowing enterprises to acquire consumer emotions and regulate their techniques accordingly.

9. Consumer Service Automation

Managing interactions with customers at scale is a developing issue for enterprises. Automated systems such as chatbots that are powered by machine learning algorithms, can tackle regular inquiries, enhancing operational competence.

These systems understand customer questions and offer responses that are similar to human agents, easing consumer issue resolution and improving satisfaction.

10. Predictive Maintenance

Predictive maintenance utilizes machine learning to analyze failures of equipment beforehand they happen. By identifying historical data from sensors, machine learning algorithms can predict when machinery will likely fail, permitting organizations to conduct maintenance only when required, reducing downtime, and minimizing the costs of maintenance. This real-world problem that can be solved with machine learning is specifically valuable in the transportation and manufacturing sectors.

Top 4 Challenges in Implementing Machine Learning

While machine learning applications are used widely to create data-based decisions, their incorporation into operations mostly faces many problems. Below are the most usual challenges faced by organizations when applying AI and machine learning use cases.

1. Insufficient Training Data

The quality of data is important for any machine learning problem. Inconsistent, insufficient, and unclean data can sternly affect the performance of ML models. Without properly organized and labeled data, it is hard to acquire meaningful outcomes from real-world machine learning examples, particularly in important areas like Machine learning in healthcare.

2. Underfitting of Training Data

Underfitting occurs when the model is too easy to capture the link between output and input variables. It is like trying to fit in a t-shirt that is too small. This particular issue makes it difficult to make suitable solutions or predictions, hence causing ineffective problem statements for machine learning projects.

3. Overfitting of Training Data

It arrives when a model is trained on so much data, which can cause poor generalization on new, and unseen data. It is like wearing oversized jeans that do not fit. This problem impacts model performance and correctness, limiting its practical implications in real-life problem statements for projects such as the detection of fraud or demand forecasting.

4. Postponed Application

Although machine learning models offer highly effective solutions, they mostly need particular processing time and power. This delay in model training and application can hamper timely decision-making. Routine monitoring and maintenance are required to keep the models performing at their best. Delayed application can affect how machine learning solves problems, specifically when fast reactions are important in sectors such as Machine learning in healthcare.

Read more: Practical Power BI Use Cases for Better Business Decisions

Wrapping Up!

As machine learning continues to develop, the scope of its applications across sectors will widen. To successfully address enterprise issues in this new era, it is very important to understand the ways machine learning applications can be applied to minimize costs, improve competence, and other excellent user experiences. However, to use machine learning efficiently within your company, it is important to collaborate with professionals who have deep domain knowledge.

If you are interested in exploring how machine learning can enhance productivity and automate your company’s enterprise procedures for your company, feel free to get in touch with us. We are here to help you explore real-world machine learning examples, analyze related machine learning problem statements, and implement AI and machine learning use cases to solve your particular real-life problem statements for projects.