Topic 3: Practical examples
Application of machine learning vast in our daily life, starting from robotic voice answering in customer support calls, voice to text conversion in whatsapp messaging, text recommendation in Google search, chatbots in any customer support chat and many more.
Below are some more practical examples:
- Face Detection: It is a is a computer technology being used in a variety of applications that identifies human faces in digital images, which uses Face-detection algorithms focus on the detection of frontal human faces. It is analogous to image detection in which the image of a person is matched bit by bit with the image stores in database. Any facial feature changes in the database will invalidate the matching process.
- Bioinformatics: Gene Prediction in Genomicsis a problem which has an increasing need for the development of machine learning systems that can automatically determine the location of protein-encoding genes within a given DNA sequence. Machine learning has also been used for the problem of multiple sequence alignment which involves aligning many DNA or amino acid sequences in order to determine regions of similarity that could indicate a shared evolutionary history. It can also be used to detect and visualize genome rearrangements
- Fraud Detection: Fraudis a billion-dollar business and it is increasing every year. Through statistical techniques and artificial intelligence, a fraud can be detected whether it happens in ecommerce or banking transactions.
- Space exploration: Space exploration is the discoveryand exploration of celestial structures in outer space by means of evolving and growing space technology. While the study of space is carried out mainly by astronomers with telescopes, the physical exploration of space is conducted both by unmanned robotic space probes, radio astronomy and human spaceflight.
- Robotics: A self driving car combines a variety of techniques to perceive their surroundings, including radar, laser light, GPS, odometry, and computer vision. Its advanced control systemsinterpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage.
- Information extraction: Information extraction (IE) is the task of automatically extracting structured information from unstructuredand/or semi-structured machine-readable In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video could be seen as information extraction.
- Document Classification: Document classification or document categorization is a problem in library science, information scienceand computer science. The task is to assign a document to one or more classes or categories. The algorithmic based classification of documents is mainly in information science and computer science. The documents to be classified may be texts, images, music, etc. Each kind of document possesses its special classification problems. Documents may be classified according to their subjects or according to other attributes (such as document type, author, printing year etc.).
- Classification of images: A topic of pattern recognitionin computer vision, is an approach of classification based on contextual information in images. “Contextual” means this approach is focusing on the relationship of the nearby pixels, which is also called neighbourhood. The goal of this approach is to classify the images by using the contextual information. if only a small portion of the image is shown, it is very difficult to tell what the image is about. However, if we increase the contextual of the image, then it makes more sense to recognize.
The only thing matters is that what is your domain of interest and how could you use machine learning in that domain?