Machine learning technology is versatile and relies on various machine learning algorithms, processes, techniques and models. Let’s look at specific use cases of machine learning to figure out how Machine Learning can be applied in your business.
Here, we will discuss the practical use case of machine learning.
1. Image & Video Recognition:
Over the past few years Image & video recognition have progressed rapidly due to advance in deep learning.
Usually, Image processing and Video recognition are used for face recognition, text detection, object recognition, logo and landmark detection, image composition, image curation and visual search.
Video recognition is the same as image recognition, both work in the same pattern. Videos get broken down frame by frame and further classified as individual digital images.
Some of the few companies who are using Image and video recognition:
Google, Facebook, Shutterstock, Pinterest, eBay, Yelp, Apple, Amazon,Salesforce.
2. Speech Recognition:
Speech recognition is another important area of machine learning that allows machines to “Mimic’ humans’ voices with help of Artificial Intelligence, Machine Learning and Deep Learning techniques.
In these techniques not only image pixel or frame by frame videos but audio files also get analyzed and processed by the help of neural networks to translate audio into a text file.
Speech recognition is used in search engines. For e.g. Google Assistance, Alexa, Siri etc
3. Fraud Detection: Machine learning plays an important role in fraud detection and there are multiple scenarios in machine learning, fraud detection belongs to a separate class of classification issues, along with spam detection.
To proactively work in fraud detection, Machine learning (ML) models need to analyze transaction details in real time and classify a given transaction as legit or fraudulent, which, given enough data is provided and isn’t that complex to do.
Machine learning helps many companies across the world save millions of money by detecting, flagging and preventing fraudulent transactions.
4. Intrusion Detection:
Machine learning (ML) intrusion detection is the lifeblood of adaptive intrusion detection systems (IDS), which monitor networks in real time to identify and cope with malicious traffic or intrusion techniques, like infiltration, brute force and unauthorized access.
Basically an IDS was designed to identify known threats. Now however, network data is continuously collected and first pre-processed to create high-quality datasets, which are further used to train machine learning models to tell normal traffic from malicious traffic in real time.
5. Recommendation Systems:
Recommenders or recommendation systems are one of the most ubiquitous machine learning applications in daily life. These systems are used in search engines like, e-commerce websites (e.g. Amazon, eBay), entertainment platforms (e.g. Netflix, Google Play), games, and multiple Web & mobile apps.
Recommender systems are usually classified by the filtering method mentioned below:
- Content-based filtering method.
- Collaborative filtering method.
Conclusion: The adoption of machine learning is increasing day by day and that’s not surprising given its benefits from eliminating manual tasks to uncovering useful insights from data and many AI, Machine Learning Deep learning companies are using GPU servers to make their task fast and easy. Let’s have a look at the top class GPU server here