Machine Learning is the AI technology that enables software applications to turn more accurate outcome predictions without requiring explicit programming. Machine Learning uses historical information as input to predict output values.
Machine Learning depends on various machine learning algorithms, techniques, models, and processes. Here are the machine learning use cases, and learn how it can be used for any business.
Machine Learning technologies
Let us learn about the practical machine learning use cases.
1. Speech recognition
Speech recognition is a significant area of machine learning that enables machines to mimic human voices with machine learning, artificial intelligence, and deep learning techniques. Processing happens with the support of neural networks to convert an audio file into a text file. With this technique, the analysis is not limited to audio files but extends to video.
Google Assistant, Siri, and Alexa are popular speech recognition tools.
2. Image recognition
Advancements in deep learning led to rapid progress in image and video recognition. Video and image recognition are usually used for face recognition, object recognition, text detection, landmark detection, and image constitution—both video and image recognition work in the same pattern. The process involves breaking down the videos to classify them as individual images.
Object or Image recognition comprises of following tasks:
1. Classifying images: Image labeling and building categories.
2. Object localization: identifying the object location in a specific image by marking it with a bounding box.
3. Detecting objects: Governing the presence of the objects with the aid of the bounding boxes and classifying it
4. Segmenting objects: Recognize and locate every item in the picture. The process does not use bounding boxes. However, it highlights the outline of the object in the image.
Machine learning applications must be thanked, for the computer vision task has achieved great success.
3. Fraud detection
Machine Learning enables to run of automated transaction processing for fraud detection. The ML model identifies fraudulent activities and raises a flag immediately. Before the professionals act on the system, the secured information is stolen. Fraudsters completely take advantage of the weakness existing in the system.
The increasing processing power and the statistical modeling advancement have allowed machine learning to operate in the banking and e-commerce sectors. Machine learning is already in use for email spam detection. Also, millions of online shoppers receive relevant product recommendations using it. Moreover, big data enables machines to enhance significantly over time.
Fraudulent transactions have certain features that legitimate transactions do not possess. That is the reason behind implementing machine learning, which detects patterns in financial operations and determines which of them is legitimate. Machine learning operations are more effective relative to humans. They can process the information quickly compared to what the best analysts can perform.
Fraudsters and businesses both use technologies in their fashion. Ml algorithms can spot the patterns that go unnoticed by humans. By studying many fraudulent behavior cases, the ML algorithms detect and keep track of the most secret fraudulent patterns. As online fraud turns high, it becomes more sophisticated. Both businesses and fraudsters use the latest technologies to compete against each other.
The prevailing consequence of such action is stress. So organizations should combat this by analyzing information to fight fraud. Despite the team of the most talented and experienced data scientists, they might need to be more capable of tracking fraudulent attempts, for they have happened very quickly over our expectations. The fraud-detecting machine learning models act 24/7 and analyze data enormously.
4. Recommendations systems
Recommendation systems recommend things to the user depending on various factors. Organizations like Amazon and Netflix use the recommendation system to help users identify the movies or products they would be interested in. These systems predict users’ most interested in or likely to purchase products.
The recommendation system manages a large volume of information by giving only relevant and crucial information depending on the user’s preferences and interests. The service providers and the users have benefited from experiencing quality decision-making.
Recommendation systems help users find their interests, generate personalized content, identify user-relevant products, help improve website engagement, and deliver the right items.
5. Intrusion detection
Intrusion Detection System is a software application to identify network intrusion using machine learning algorithms. The intrusion detector is to develop a predictive model capable of detecting the good and the wrong connections. Gaussian Naive Bayes, Random Forest, Decision Tree, and Logistic Regression are the various algorithms used for intrusion detection.
Machine learning adoption is enhancing daily, and surprisingly, it has eliminated manual tasks to discover valuable insights from the data. Machine Learning and Deep Learning organizations use the most advanced servers to make their jobs easy and quick.