REAL-LIFE EXAMPLES OF MACHINE LEARNING

Intelligent systems based on machine learning algorithms can learn from past experience or historical data. Machine learning applications deliver results based on past experience. In this article, we'll discuss 10 real-world examples of how machine learning can help develop better technologies to drive ideas today

SkyInfotech is one of the best institutes in Noida for Machine Learning training for the last 17 years and in this I will talk about some real life example of ML.

IMAGE RECOGNITION

Image identification is one of the most popular uses of machine learning. There are many circumstances in which you can classify the object as a digital image. For example, in the case of a black and white image, the intensity of each pixel served as one of the dimensions. In color images, each pixel offers three intensity dimensions in three different colors - red, green, and blue (RGB).Machine learning can also be used for facial recognition in an image. There is a separate category for each person in a multi-person database. Machine learning is also used for character recognition to recognize both handwriting and printed letters. We can split a font into smaller images, each containing a single character.

VOICE RECOGNITION

Speech perception is the interpretation of spoken words into text. It is also recognized as machine speech recognition or electronic speech recognition. Here, a software application can identify the words spoken in an audio clip or audio file and then convert the audio to a text file. The strength in this application can be a set of numbers that outline the speech signal. We can also divide the speech signal by intensities across different frequency bands.

Speech recognition is used in applications such as the voice user interface, voice search, and more. Voice user interfaces include voice dialing, voice calls, and voice control. It can also be used as a simple data entry and preparation of organized documents.

MEDICAL EXAMINATION

Machine learning can be used in techniques and tools that can help diagnose diseases. It is used to evaluate clinical parameters and their combination, for example, to predict the course of the disease, in order to obtain medical knowledge for result research, for therapy planning and patient monitoring. This is the successful implementation of machine learning methods. This will help integrate computerized systems into the health sector.