Machine learning is the study of making machines more human-like in behavior and decisions by allowing them to learn and develop their own programs.
The term Machine learning was coined by Arthur Samuel in 1959 with the definition, “A field of study that gives computers the ability to learn without being programmed explicitly.” Then, what are examples of machine learning in everyday life?
Machine Learning Projects Applications
Examples of machine learning actually exist in various forms that are very familiar with everyday activities. Starting from transportation, technology, finance, education, health, and social media that you frequently visit.
1. Transportation
An example of machine learning in transportation is simplifying travel times to be shorter. For example, Google Maps, which uses location data from a smartphone, can check shifts in traffic flow at any time in real time.
Moreover, it can also detect traffic reports such as jams and accidents. By accessing relevant data, Google Maps can reduce travel time by showing the fastest route.
In addition, machine learning can also analyze estimated travel prices, accurately pick up locations, optimize the shortest routes, and detect fraud. Programs like this are beneficial for online transportation services. Uber has used it to maximize benefits.
Machine Learning Projects Examples:
Self-Driving Car
The development of car technology that can drive independently (self-driving car) is increasingly advanced.
Self-driving cars leverage machine learning and artificial intelligence for continuous education by studying various road conditions, bends, obstructions, and traffic signs.
2. Banking and Personal Finance
Financial companies such as banks use machine learning for several functions, such as fraud prevention, credit, and m-banking. Usually, daily transactions are very high, and it is difficult for humans to manually review each transaction.
Machine learning-based systems create a neural network to determine whether a transaction is fraudulent. You can see the factors from the frequency of recent transactions, transaction size, and the types of retailers.
The decision to accept credit applications has now also used AI. When applying for credit or loans, financial institutions must quickly determine whether to approve them. Machine learning assists in credit decisions based on individual user-specific risk assessments.
AI tech can also create valuable services like personalized mobile banking to become more efficient for users. They don’t need to go to the bank or ATM, but just one click away on their cellphone.
M-banking allows users to check balances, transactions, transfers, and other bank services with a smartphone.
3. Education
Academic activity is always inseparable from essay work, and checking plagiarism is a standard for essay originality. An example of machine learning in education is building an accurate plagiarism detector.
It can quickly analyze numerical estimates of how similar the input documents are to other records in the database.
Machine learning can also create Robo-readers, which are automatic essay-scoring systems. Essays are very complex writing, but thanks to the help of AI, grading essay assignments has become much more manageable.
A GRE exam uses a human assessor and a Robo-reader known as an e-Rater to grade essays. Even in this education sector, machine learning helps in personalized learning, and voice assistants facilitate administration and analyze a student’s dropout rate.
Machine Learning Projects Examples:
Turning Handwritten Documents into Digitized Versions
This project offers a hands-on experience with deep learning and neural networks, which are key concepts in image recognition. It also provides an introduction to the process of transforming pixel data into images and the use of logistic regression and MNIST datasets, making it a great choice for beginners in the field of machine learning.
4. Health
An example of machine learning in the medical field is dealing with diagnostic and prognostic problems. It can analyze medical data regularly, delete invalid data, annotate medical unit-generated data, and monitor patients effectively.
Patient management and administrative affairs work become much more efficient.
Machine Learning Projects Examples:
Breast Cancer Prediction
This project utilizes a dataset to predict the probability of a breast tumor being malignant or benign, based on factors such as lump thickness, number of bare nuclei, and mitosis.
It is a valuable opportunity for new machine learning professionals to gain experience with R programming.
5. Social Media
When a photo uploading on Facebook, friend tag suggestions will automatically appear based on facial detection in the picture. That’s AI.
Besides facial identification, Facebook also uses AI to personalize feeds and display posts that entertain or attract users’ attention based on interests. This also applies to serving certain business advertisements that match the user’s interests.
Instagram, Pinterest, and Snapchat applications also use machine learning to recognize objects in images. Snapchat and Instagram’s face filters (Lens) can filter and track facial activity, allowing animation or digital filter masks to follow the movements of the user’s face.
Machine Learning Projects Examples:
Home feature on Facebook, Instagram, and Twitter.
The following example of a machine learning application is the Facebook homepage or Twitter timeline. Facebook displays posts from our closest friends or posts with topics we are interested in to increase engagement.
Machine learning is also used to search for people, which appears in the section: “People you may know.”
Sorting Spesific Tweets of Twitter
Filtering tweets containing specific keywords and information can be a tedious task.
This beginner-level machine learning project allows programmers to develop an algorithm that uses scraped tweets processed through natural language processing to identify those that match certain themes, mention certain individuals, etc.
6. Smart Personal Assistant
Of course, you are familiar with Siri, Google Assistant, Amazon Alexa, and Google Home. Technology is our closest example of machine learning.
By fully implementing AI, home devices and personal assistants will follow commands, including setting reminders, searching for information online, adjusting lights, and so on.
Machine Learning Projects Examples:
Google search autosuggest
We start with the most familiar: Google. Indeed we are all aware that the Google Search Bar has an autosuggest or autocomplete feature.
This feature gives the word or phrase recommendations even before we finish typing. For example, we type the word: “place to eat.” Then Google will provide recommendations: the nearest restaurant, where to eat in Bogor, and so on.
Amazingly, these recommendations can vary depending on our history and preferences. The recommendation process through autosuggest is an example of implementing machine learning.