Introduction
Machine learning is a rapidly growing field, and if you’re looking for a way to get started, this list is a great place to start. We’ve compiled some of the best machine learning applications out there and the type of work they require so you can see how they can be applied in real life.
Recommendation Engines
Recommendation Engines
Recommendation engines are used to suggest items that you might be interested in. They can be used for personalization, e.g., suggesting items based on previous purchases or browsing history. Recommendation engines can also improve search results by providing similar items to those already found (or at least related ones).
Image Processing
Image processing is a very broad field, and there are many different applications for it. For example, image processing can be used to detect faces, recognize objects and even detect emotions.
Image processing is used in many different industries and applications including:
- Law Enforcement – Image analysis software is used by police forces around the world to help them identify criminals from CCTV footage or photographs taken at crime scenes. This technology has been proven to be more accurate than human eyesight alone by identifying suspects who might otherwise have gone undetected by law enforcement agencies!
- Security – Surveillance cameras have become an essential part of keeping our cities safe from crime but they still need someone watching them 24/7 which means there’s plenty of opportunity here for machine learning algorithms trained on images taken from these cameras (or even just written descriptions) so they can monitor what’s going on without needing any human intervention at all beyond initial setup time!
Natural Language Processing
Natural Language Processing (NLP) is a subfield of machine learning that focuses on making computers better at understanding human language. NLP has many applications, including search engines, chatbots and voice assistants.
It’s also a great place to start if you want to get into machine learning because it involves building models from data–a key concept in all forms of ML–and then using those models to make predictions about new data sets.
Search and Query Processing
Search and query processing is the use of machine learning to improve search results. This includes improving the quality of results and making it easier for users to find what they’re looking for.
Google, Bing, Amazon and other major search engines use machine learning algorithms to continuously improve their search results. These algorithms can analyze millions of queries from real users in order to understand which ones are most relevant and then adjust the ranking accordingly.
This type of application has been around since Google launched its first version in 1997 (back then called BackRub). Since then we’ve seen many improvements including PageRank, AdWords targeting and spam filtering all powered by ML!
Fraud Detection and Prevention
Fraud detection and prevention is a common use case for machine learning. In fact, it’s one of the most obvious ways to apply this technology because fraud detection can be done in so many ways with machine learning.
One example is identifying suspicious patterns in data and flagging them as potential fraud cases. Another example would be detecting anomalies in data that indicate abnormal behavior–say someone has made thousands of purchases on their credit card without paying off any balances, or if there are huge swings in spending between months (e.g., $100/month vs $300/day).
Machine learning is a rapidly growing field and if you’re looking for a way to get started, this list is a great place to start.
If you’re looking for a way to get started, this list is a great place to start. This is not an exhaustive list and there are many other applications of machine learning that could be listed here.
Conclusion
Machine learning is a rapidly growing field and if you’re looking for a way to get started, this list is a great place to start.
