Numerous examples of machine learning show that machine learning (ML) can be extremely useful in a variety of crucial applications, including data mining, natural language processing, picture recognition, and expert systems. In all of these areas and more, ML offers viable solutions, and it is destined to be a cornerstone of our post-apocalyptic civilization.
The history of machine learning shows that a good grasp of the machine learning lifecycle increase machine learning benefits for businesses significantly. There are many uncommon machine learning examples that prove this, and you will find the best ones in this article.
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Are you looking for examples of machine learning?
Machine learning uses statistical methods to increase a computer’s intelligence, assisting in the automatic utilization of all business data.
Due to growing reliance on machine learning technologies, humans’ lifestyles have undergone a significant transformation. We all use this subset of artificial intelligence, whether consciously or unconsciously. We use Google Assistant, which uses ML principles, as an example. We also use online customer assistance, another machine learning application. However, it would be beneficial to quickly review ML types before going on to machine learning scenarios.
Types of machine learning
In order for a machine to learn and develop predictions, look for patterns, or categorize data, a significant volume of data must be presented to it. The machine learning type, which functions somewhat differently, is determined by the algorithm that is applied. Supervised learning, unsupervised learning, and reinforcement learning are the three types of machine learning.
The reason this type of machine learning is called “supervised” learning is that you feed the algorithm information to aid in learning while it is being “supervised.” The remainder of the information you supply is used as input features, and the output you give the system is labeled data.
Several commercial goals, such as sales forecasting, inventory optimization, and fraud detection, can be accomplished by supervised learning. Use cases include, for instance:
- Estimating the price of real estate.
- Determining the degree of fraud in bank transactions.
- Identifying illness risk elements.
- Assessing the riskiness of potential borrowers for loans.
- Predicting the failure of mechanical components in industrial equipment.
The benefit of unsupervised machine learning is that it can use unlabeled data. This means that no human intervention is needed to make the dataset machine-readable, enabling the program to function on much larger datasets.
The labels in supervised learning give the algorithm the ability to determine the precise type of relationship existing between any two data points.
Several instances of use cases include:
- Group customers based on their buying habits.
- Group inventories based on manufacturing and/or revenue metrics.
- Identifying relationships in customer data (for example, customers who buy a specific style of handbag might be interested in a specific style of shoe).
The machine learning method that most closely resembles how people learn is reinforcement learning. By interacting with its environment and receiving rewards, either good or negative, the algorithm or agent being employed learns. Deep adversarial networks, Q-learning, and temporal differences are examples of common algorithms.
Examples of certain uses are as follows:
- Teaching vehicles how to park and drive themselves.
- Adjusting traffic lights dynamically to ease congestion.
- Using unprocessed video as input to teach robots how to follow rules so they can copy the behaviors they observe.
We will give more examples of machine learning types in the following section.
15 real-life examples of machine learning in everyday life and business
The normal human may feel intimidated, confused, and possibly intangible when thinking about machine learning, conjuring up visions of ominous robots doing havoc on the planet. Machine learning is happening every day in front of and around us, whether we see it or not, as more businesses and individuals depend on the models to manage huge volumes of data. Let’s look at some of them:
One of the more obvious uses of machine learning is facial recognition. People used to receive name recommendations when they tagged someone in their smartphone images or on Facebook, but now tags are instantly validated by comparing and analyzing patterns in facial shapes. Additionally, the combination of facial recognition with deep learning is now widely used in the medical field to precisely track a patient’s medical use or diagnose hereditary illnesses.
Additionally, it is employed in the fight against significant societal problems including child sex trafficking and child sexual exploitation. It is increasingly being used in more applications and sectors.
Targeted marketing in the retail industry groups customers based on demographic or purchasing patterns and extrapolates what one person might want from another purchase. While some recommended purchase combinations are clear, machine learning may become startlingly precise by uncovering hidden links in data and foretelling what you desire before you even realize it. It is one of the most used examples of machine learning.
For instance, Coca-Cola is one of the largest beverage firms in the world because of its vast product line and global market—more than 500 drink brands are offered there. The corporation not only generates a lot of data, but it has also embraced new technology and uses that data to drive the development of new products, profit from AI bots, and even test augmented reality in bottling operations.
New songs are currently being inspired by algorithms for creating music. There are insights that can be derived from enough data, such as millions of conversations, newspaper headlines, and speeches, to help develop a lyrical theme. A variety of musical elements can be generated by computers like Watson BEAT, which can provide inspiration to songwriters. AI assists musicians in understanding the preferences of their fans and in making more precise predictions about which songs will ultimately be hits. It is one of the most interesting examples of machine learning.
Healthcare and medical diagnosis
In medicine and healthcare, machine learning addresses prognostic and diagnostic challenges. A few of the various applications of machine learning in healthcare include disease breakthroughs, patient monitoring and management, medical data analysis, and management of incorrect medical data. It is one of the most promising examples of machine learning.
For example, Omdena has used sequential and static feature modeling with recurrent neural networks (RNNs) to predict cardiac arrest.
For high-quality produce, machine learning in agriculture provides accurate and effective farming with minimal labor. Additionally, machine learning offers priceless crop-related information and suggestions so that farmers can reduce losses.
In order to increase food security in Senegal, Africa, Omdena developed a crop yield forecast tool using satellite photos from Google Earth Engine (GEE) pictures and Jupyter.
One of the most essential uses of machine learning is sentiment analysis. A real-time machine learning application called sentiment analysis works to ascertain the sentiment or viewpoint of the speaker or writer. For instance, a sentiment analyzer will quickly determine the true intention and tone of a review or email (or any other type of document) that has been written. This sentiment analysis tool can be used to examine decision-making applications, review-based websites, etc.
Virtual personal assistants: Is Siri a machine learning?
Some of the well-known examples of virtual personal assistants include Siri, Alexa, and Google Now. When asked over the phone, they provide assistance in discovering information, as the name implies. Simply activate them and ask them things like “What is my schedule for today?” or “What are the flights from Germany to London?” Your personal assistant searches for the information, remember your pertinent questions, or issues a request to other sources (such as phone apps) to gather the information. It is one of the most used examples of machine learning.
Even more, you can provide instructions to your assistants, such as “Set an alarm for 6 AM the next morning” or “Remind me to visit the visa office the day after tomorrow.”
Social media services
Social media firms are adopting machine learning for their own and user benefits, from customizing your news feed to better ad targeting. “People you may know” and “similar pins” features are just a few instances of things you must be using, recognizing, and adoring on social media without realizing that they are nothing more than machine learning (ML) applications.
Big data and artificial intelligence-related ML are also used by Instagram to target advertising, stop cyber bullying, and remove abusive comments. Artificial intelligence is essential to the platform’s ability to display users stuff they might be interested in, combat spam, and improve the user experience as the platform’s content volume increases.
Email automation and spam filtering
While it might appear that your mailbox is quite uninteresting, machine learning actually affects how it works. Successful machine learning directly leads to email automation, and spam filtering is one of its most utilized features. Successful spam filtering learns from its mistakes and recognizes unwelcome email content patterns. This includes information from email domains, the physical address of the sender, the text and structure of messages, and IP addresses. It is one of the most everyday life examples of machine learning.
Additionally, users must assist since they must flag emails that have been incorrectly filed. Every time an email is marked, a new data reference is added to aid with future accuracy.
The banking sector has benefited from machine learning as most systems are becoming digital. Machine learning makes it simple to examine large volumes of financial transactions that are invisible to the human eye and assists in identifying fraudulent transactions. It is one of the most crucial examples of machine learning.
110 million AmEx cards are in use, and American Express conducts $1 trillion in annual transactions. To detect fraud in almost real-time and prevent millions of dollars in damages, they mainly rely on data analytics and machine learning algorithms. AmEx is also using its data flows to create apps that can connect cardholders with goods and services as well as exclusive deals. Additionally, they provide trend analysis for online businesses and industry peer benchmarking for retailers.
Online customer support
Today, many websites give visitors the option of chatting with a customer service agent as they browse the site. But not every website has a live representative available to respond to your inquiries. You converse with a chatbot the majority of the time. The information that these bots tend to extract from the website and deliver to the clients. The chatbots are developing over time. Due to its machine learning algorithms, they have a tendency to better understand customer queries and provide them with better replies.
Search engine results
Machine learning is used by Google and other search engines to enhance your search results. The algorithms at the backend monitor how you react to the results after each search you conduct. The search engine considers that the results it presented were relevant to the query if you open the top results and browse the page for a while.
The search engine assumes that the results it served did not meet your requirements if you reach the second or third page of search results but do not open any of them. The algorithms at the backend enhance the search results in this way.
Language translation is one of the most frequently used applications of machine learning. In the translation of one language to another, machine learning is important. We are astounded by how easily websites can translate between languages while still providing context. Machine translation is the name of the technology that powers the translation tool. It is one of the most useful examples of machine learning.
Without technology, life would not be as simple as it is now because it has made it possible for people to engage with people from all over the world. It has given tourists and business partners the assurance they need to travel securely abroad since they know that language will no longer be an obstacle.
Some people might be reminded of the chess match between IBM’s Deep Blue and Gary Kasparov, in which Deep Blue prevailed. Or in 2016, when Lee Dedol, the Go world champion, was defeated by Google DeepMind’s AlphaGod. ML features
Self-driving cars and automated transportation
Did you know that a Boeing 777 pilot only spends seven minutes actually flying the aircraft? Today’s flights use FMS (Flight Management System), a GPS, motion sensor, and computer system combination that tracks its position as it flies. However, when we attempt to apply the same idea to automobiles, the dynamics significantly alter. There are other vehicles on the road, hazards must be avoided, and restrictions that are governed by traffic laws. It is one of the most “Elon Musk” examples of machine learning.
Nevertheless, self-driving vehicles already exist. A study using 55 Google vehicles that have collectively logged more than 1.3 million miles in driving suggests that these AI-powered automobiles may perform better than their human counterparts. The usage of Google Maps, which sources location-based information, has already resolved the navigational challenges.
Which is not an example of machine learning?
It’s critical to grasp what machine learning is not in order to comprehend what it actually is. As a result of the frequent use of the terms “artificial intelligence,” “machine learning,” “deep learning,” and “statistical learning,”
Consider Netflix to fully grasp the distinction between statistical learning and machine learning. The Netflix Prize, a competition for the best recommendation system, was introduced by the corporation in 2006. Contestants could approach this task using statistical learning or machine learning, as Brian Caffo suggested.
Artificial intelligence is what machine learning is. However, machine learning is not artificial intelligence. This is so because artificial intelligence includes machine learning. Artificial intelligence also includes areas like computer vision, robotics, and expert systems in addition to machine learning.
On the other hand, deep learning and machine learning are sometimes confused because deep learning is a subtype of machine learning. Machine learning and deep learning both provide methods for classifying data and training models. Their differences can be seen in the way they approach learning. When using machine learning, you upload data (such as photographs), manually describe the characteristics, build a model, and the computer then predicts the future. You can omit the process of manually defining features while using deep learning. Deep learning algorithms work with data directly. This is a self-teaching system that was trained using many neural networks and a large number of data sets.
Machine learning implementation
You must select the platform, IDE, and programming language before beginning to create ML applications. Numerous options are available. Since all of them offer the implementation of the AI algorithms outlined thus far, the majority of them would easily satisfy your needs.
The aforementioned details must be thoroughly comprehended if you are creating the ML algorithm on your own:
- The language of your choice is, essentially, how well-versed you are in one of the languages used in machine learning development.
- The IDE you use will rely on your comfort level and experience with the available IDEs.
The languages that facilitate ML development are listed below:
Although not entirely exhaustive, this list includes numerous widely used languages for machine learning development. Choose a language for the development, create your models, and run tests based on your level of comfort.
The IDEs that support ML development are listed below.
- R Studio
- iPython/Jupyter Notebook
- Google –Colab
The list provided above is not entirely exhaustive. Each has advantages and disadvantages of its own. Before settling on a single IDE, the reader is advised to test out these options.
The following list of platforms includes those where ML applications can be used:
- Microsoft Azure
- Google Cloud
Again, this list is not all-inclusive. It is suggested that the reader sign up for the aforementioned services and give them a try.
What is the best language for machine learning?
There is no ideal language for machine learning; each works well depending on the situation. It’s true that there isn’t just one machine learning language that works well. However, certain programming languages are undoubtedly more suited than others for machine learning applications. According to the type of business challenges they are working on, many machine learning engineers select a machine learning language.
No matter what a person’s personal preferences are for a particular programming language, we have outlined the top five for machine learning:
- R Programming Langauge
Businesses outside of the AI sector, such as those in retail, logistics, and transportation, already benefit from machine learning’s enhanced effectiveness and untapped potential. Humans have undergone significant lifestyle changes as a result of machine learning technologies, on which we rely heavily. We all use it, whether intentionally or unintentionally
In addition to the cases mentioned above, machine learning has demonstrated its potential in a number of other contexts. Share your thoughts about machine learning and how it has affected your daily life in the comments section below.