5 Best Examples Of Machine Learning In Real Life!

As we are so reliant on machine learning technology, it has drastically altered our way of life. It is a subset of Artificial Intelligence that we all use, intentionally or unknowingly. For example, we utilize Google Assistant, which employs ML ideas, and we seek assistance from online customer support, which is also an example of machine learning.

 

Machine Learning employs statistical approaches to make a computer more intelligent, allowing it to get and utilize large corporate data sets automatically as needed. There are numerous real-world examples of Machine Learning, including the following, but before let's understand what machine learning actually is. 

 

Introduction to Machine Learning

 

Machine Learning tutorial covers both fundamental and advanced machine learning principles. Our machine learning tutorial is intended for both students and experts in the field.

 

Machine learning is a developing technique that allows computers to learn autonomously from historical data. Machine learning employs a variety of algorithms to construct mathematical models and make predictions based on past data or information. It is being utilized for a variety of activities including image recognition, audio recognition, email filtering, Facebook auto-tagging, recommender systems, and many others.

 

This machine learning course introduces you to machine learning as well as several machine learning approaches such as supervised, unsupervised, and reinforcement learning. Regression and classification models, clustering approaches, hidden Markov models, and other sequential models will be covered. In the real world, we are surrounded by humans who have the ability to learn from their experiences, as well as computers or robots that function on our orders. Can a machine, like a human, learn from past experiences or data? So now comes Machine Learning's part.

 

Machine Learning is defined as a subset of artificial intelligence that is primarily concerned with the development of algorithms that allow a computer to learn on its own from data and previous experiences. Arthur Samuel coined the phrase "machine learning" in 1959. 

 

Also Read | 8 Real-Life Applications of Image Recognition

 

Why Machine Learning?

 

Machine learning is significant because it provides organizations with insights into trends in customer behavior and business operating patterns, as well as assisting in the development of new products. Machine learning is fundamental to the operations of many of today's biggest organizations, like Facebook, Google, and Uber. For many businesses, machine learning has become a crucial competitive differentiation.

 

Machine learning isn't as difficult to grasp as you would assume. In a nutshell, it entails utilizing pattern recognition software to identify trends in data, developing models to explain the trends/patterns, and then using the models to forecast anything. The more a computer programme "learns" about one data set, the better it predicts the outcome of another.

 

If you provided a machine learning algorithm a bunch of photographs with flowers or humans in them, it would learn from the labeled data and be able to tell whether the next image it processed was a flower or a person. In effect, it improves with use because each new piece of data represents a "learning" opportunity for the machine. Machine learning is being applied in a wide variety of applications. The recommendation engine that runs Facebook's news feed is perhaps one of the most well-known examples of machine learning in operation.

 

Machine learning is used by Facebook to personalize how each member's feed is delivered. If a member often pauses to read the posts of a specific group, the recommendation engine will begin to show more of that group's activity earlier in the feed.

 

Classification Of Machine Learning

 

Machine learning can be broadly categorized into three types:

 

1. Supervised learning:

 

Supervised learning is a form of machine learning method in which we feed sample labeled data to the machine learning system in order to train it, and it predicts the output based on that. The system builds a model using labeled data to interpret the datasets and learn about each data. After training and processing, we test the model by supplying sample data to see if it predicts the precise output or not.

 

The purpose of supervised learning is to connect input and output data.  The supervised learning is based on supervision, just like when a student learns something under the observation of a teacher. Spam filtering is one example of supervised learning.

 

2. Unsupervised learning:

 

Unsupervised learning, unlike supervised learning, does not employ the same labeled training sets and data as supervised learning. Instead, the machine searches the data for less evident patterns. This type of machine learning is quite useful when it comes to identifying patterns and making judgements based on data. Unsupervised learning techniques commonly employed include Hidden Markov models, k-means, hierarchical clustering, and Gaussian mixture models.

 

As an example from supervised learning, suppose you didn't know which clients defaulted on loans. Instead, you would provide the machine borrower data, and it would hunt for patterns between borrowers before categorizing them into multiple clusters.

 

This type of machine learning is commonly utilized in the development of prediction models. Clustering, which builds a model that groups items together based on specified qualities, and association, which finds the rules that exist between the clusters, are two other common applications. Here are a few examples of use cases:

 

Making consumer groupings based on purchasing habits. Inventory classification based on sales and/or manufacturing metrics.

 

3. Reinforcement learning:

 

Reinforcement learning is the form of machine learning that is most similar to how people learn. The algorithm or agent learns by interacting with its surroundings and receiving a positive or negative reward. Algorithms that are commonly used include temporal difference, deep adversarial networks, and Q-learning.

 

Returning to the bank loan client example, you may examine customer data using a reinforcement learning system. If they are classified as high-risk by the system and default, the algorithm receives a positive reward. If they do not default, the algorithm is rewarded negatively. In the end, both cases help the machine learn by better understanding the problem and its surroundings. 

 

According to Gartner, most ML platforms do not support reinforcement learning because it takes more processing capacity than most organizations have . Reinforcement learning is useful in regions that can be completely simulated and are either immobile or contain vast amounts of relevant data. This type of machine learning is considered easier to work with when dealing with unlabeled data sets because it involves less management than supervised learning. This type of machine learning is still finding practical applications.

 

This branch of artificial intelligence focuses on leveraging data and algorithms to replicate human learning, allowing machines to grow over time, becoming more accurate when generating predictions or classifications, or revealing data-driven insights. It operates in three fundamental methods, beginning with the use of a combination of data and algorithms to forecast patterns and categorize data sets, followed by an error function that aids in evaluating the accuracy, and finally an optimisation process to best fit the data points into the model.

 

Also Read | 10 Applications of Machine Learning in Social Media

 

Examples of Machine Learning

 

Machine learning has become so pervasive in our lives that you may not have noticed technology that uses it to understand your preferences, predict traffic patterns, and recognise speech and photos. Machine learning algorithms learn more and become more accurate as they are fed more data.

 

Machine learning is inextricably linked to the technology we use every day in the modern world. Here are some machine learning examples:

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Examples of Machine Learning

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1. Image and Speech Recognition:

 

Speech to text conversion is aided by computer speech recognition or automatic speech recognition. Many programmes convert live speech to an audio file format, which is then converted to a text file.

 

Real-world applications of speech recognition include voice search, voice dialing, and appliance control. The most popular speech recognition software is Alexa and Google Home. Image recognition, like speech recognition, is the most extensively used example of Machine Learning technology for identifying any object in the form of a digital image. There are some real-world applications of image recognition, for example,

 

As we've seen on Facebook, tagging the name on any photo. It is also utilized in handwriting recognition by breaking down a single letter into smaller pictures.

 

2. Google Maps traffic alerts:

 

Google Maps is one of the most commonly utilized tools when attempting to locate a specific location. The map assists us in determining the best or fastest route, traffic, and much more information. But how does it provide this information to us? Google Maps employs a variety of technologies, including machine learning, which takes information from various users, analyzes that information, updates it, and makes predictions. It can also notify us about traffic before we start our travel using predictions. While stuck in traffic, machine learning can assist us choose the best and quickest route using Google Maps.

 

3. Online Customer Support Chatbot:

 

A chatbot is the most commonly utilized software in every area, including banking, medical, education, and health. Chatbots can be found in any banking application to provide speedy online service to customers. These chatbots also use Machine Learning ideas. Based on frequently requested questions, the programmers input some fundamental questions and answers. So, anytime a consumer asks a question, the chatbot searches a database for the keywords in the inquiry and then responds appropriately. This aids in providing clients with rapid and efficient customer service.

 

4. Product suggestions:

 

Product recommendations are one of the most common applications of machine learning, appearing on the majority of e-commerce websites. Websites track your behavior using machine learning models based on your surfing habits, recent transactions, and shopping cart information.

 

Similar machine learning algorithms are used by Spotify and Netflix to propose music or TV programmes based on your previous listening and viewing behavior. These algorithms learn your preferences over time and can recommend new artists or films you might love.

 

5. Personal virtual assistants:

 

Virtual personal assistants are gadgets that you may have in your house, such as Amazon's Alexa, Google house, or Siri on the Apple iPhone. These gadgets employ a combination of speech recognition technology and machine learning to collect data on what you're asking for and how frequently the device delivers accurate results. They recognise when you begin speaking, what you're saying, and carry out your command.

 

For example, if you ask Siri, "Siri, what is the weather like today?" She will search the internet for weather forecasts in your area and deliver detailed information.