11 Examples of machine learning in real life - Newport Paper House

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11 Examples of machine learning in real life

Artificial intelligence (AI) is growing by leaps and bounds, with an estimated market size of $7.35 billion. Machine learning (ML) is an area of AI that improves our daily lives in various ways. ML involves a group of algorithms that allow software systems to be more accurate and precise in predicting outcomes.

Machine learning has been at the forefront in recent years due to impressive advances in computer science, statistics, development of neural networks, and improvements in the quality and quantity of data sets. . Here we look at machine learning examples to give you a better perspective. In particular, we'll look at real-world examples of machine learning impacting and aiming to make the world a better place.

What is machine learning?

Arthur Samuel, the pioneer of artificial intelligence in the 1950s, coined the term "machine learning." Here the focus is on using data and algorithms to mimic the way humans learn and gradually improve their accuracy.

Many machines today learn from real world examples. We may or may not know that machine learning is used in various applications such as voice search technology, image recognition, machine translation, self-driving cars, etc.

The four types of machine learning algorithms?

As we mentioned earlier, machine learning algorithms allow machines to identify patterns in the data and in turn learn from the training data. Before we get to the machine learning examples in python or our featured real-world machine learning examples, let's look at the four main types of machine learning with examples.

Supervised teaching

In supervised learning, we feed the algorithm output to the system so that the machine knows the patterns before working on them. In other words, the algorithm is trained on input data that has been tagged for a particular output. The model is trained until it can detect underlying patterns and relationships between input data and output labels, allowing it to produce accurate labeling results when presented with unpublished data.

Semi-supervised learning

Here the approach involves supervised machine learning using labeled training data and unsupervised learning using unlabeled training data.

Unsupervised learning

The unsupervised learning approach is great for discovering relationships and insights in unlabeled data sets. Therefore, inferences are made based on circumstantial evidence without training or guidance. Machine learning clustering examples are included in this learning algorithm.

Reinforced learning

The reinforcement learning approach in machine learning determines the best path or option to select in situations to maximize reward. Key examples of machine learning in everyday video games use this approach. Apart from video games, robotics also uses models and reinforcement algorithms. Here is another example, where we at Omdena have created a content communication prediction environment for marketing purposes.

11 Examples of ML in real life

1. PayPal

Neural networks and machine learning algorithms allow you to collect and analyze large data sets: exact dates and times of transactions, geographic location, customer information, and customer behavior. In-depth training technologies are used in PayPal's online payment system: To protect customers, the company has developed a large-scale system to collect and analyze behavioral patterns.

By implementing deep learning techniques, PayPal analyzes customer data and assesses risk more effectively.

2. Netflix

Machine learning is integral to the process of finding the most relevant TV shows based on user data and preferences.

By analyzing the ratings, Netflix can understand which movies to recommend to other "like" users.

Recently, to improve the user experience, Netflix has even started choosing covers for content that is more appealing to a particular viewer. Netflix's development department has outlined how the personalization algorithm works. It can show an actor or a dramatic moment according to the user's taste that is detected by machine learning algorithms.

3. Farming

Machine learning Applications in agriculture enables precise and efficient farming with less labor for high-quality production. Machine learning also provides invaluable crop insights and recommendations so farmers can minimize their losses.

4. Cyber security

Apps like PayPal and GPay use machine learning to track transactions and differentiate between illegitimate and legitimate transactions. In this way, machine learning maximizes cybersecurity by preventing online monetary fraud.

In addition to the applications listed above, there are other major industries and domains that implement ML technologies.Omdena manages AI projects with organizations that want to get started with AI, solve a real problem, or create solutions that can be implemented in two months.

5. Google maps

It's no surprise that the search giant is using machine learning to help us find things faster on the internet. Machine learning technology has recently been extended to Google Maps, improving the usability of the service.

At the beginning of February 2017, Google launched a new function in the Google Maps service, which allows it to determine the parking workload. To teach the algorithm, Google developers studied data on how easily drivers "find" a parking spot and measured how long they spent in the process. After that, the company deleted irrelevant data: drivers who stayed in private parking lots and taxi drivers. Google has determined that if drivers are circling the same area, it means it's quite difficult to find a parking space.

6. Face detection in images

Machine learning finds its application in the detection of faces in the midst of non-facial objects, such as buildings, landscapes, or other parts of the human body, such as legs or hands. It plays a crucial role in improving surveillance techniques by tracking down terrorists and criminals to make the world safer.

7. Health and medical diagnosis

Machine learning deals with prognostic and diagnostic problems in medicine and healthcare. Advances in disease, patient tracking and management, medical data analysis, and handling of inappropriate medical data are just a few of the many examples of machine learning in healthcare.

Read more : Cost to develop on-demand Medicine Delivery App

8. Government industry and policy making

The use of machine learning helps authorities track and manage the massive amount of data generated by public surveillance devices. Real-time data analysis for anomalies and threats by law enforcement helps track criminals and missing children. This makes Internet Service Providers more successful in identifying instances of suspicious online activity indicative of child exploitation.

Another example is where a team of data scientists and ML engineers from Omdena successfully applied machine learning to improve public sector transparency by enabling greater access to government contracting opportunities.

9. Spotify

Among all music services, Spotify became the first company to combine multiple song analytics models. If you're one of those 100 million users who just opened a new playlist to check out what exactly Spotify has prepared for you, you might want to know that machine learning algorithms are behind it. This is a solo mix of thirty songs you've never heard, but probably like.

Spotify knows your music tastes better than anyone. Every week you can discover the selection of excellent tracks that you would never have found on your own.

10. Uber

The simpler something is on the surface, the more complex logic lurks within it. It is very easy to be punctual with Uber. We owe this to the machine learning algorithms Uber uses to determine arrival time and pickup location. The technology processes previous trips made and uses this data to estimate the result that applies to your trip.

Uber's ML platform is called Michelangelo. Covers the entire ML workflow: Uber engineers can benefit from automated data management, training, analytics, and predictions.

11. Facebook

The amount of data processed daily on Facebook servers is rocketing into the stratosphere. Nearly 1/6 of the world hangs out on FB for about 50 minutes. Every day. However, when you enter FB, it is not a foreign place but "your territory". Why then?

Facebook was one of the pioneers that started applying machine learning and AI technology in its search and suggestion algorithms. They introduced FBLearner Flow, a machine learning platform that could take data, produce machine learning models, send the information to FBLearner's predictor, and send the information back to the system. The information is then used in Facebook products such as search, ads, and news.

Author bio:

I’m Srija Kalavala, a fascinating Technical Content writer currently working at “Hyena Information Technologies”, one of the Best Artificial Intelligence Companies in Bangalore. Interested to know about technology updates. Mobile App Development, IoT, Artificial Intelligence, Machine Learning, IOS, and Technology-related content Get connected with me on Linkedin.

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