Machine learning (ML) is important because this technology allows companies to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data.
If any corrections are identified, the algorithm can incorporate that information to improve its future decision making.
Machine learning technology has risen to prominence in many sectors today. For example, banks and other lenders use ML classification algorithms and predictive models to determine who they will offer loans to.
Many stock market transactions use machine learning. AI and ML use decades of stock market data to forecast trends and suggest whether to buy or sell.
ML developments have led to training machines in pattern recognition, which is now sometimes used in radiology imaging. AI-enabled computer vision is often used to analyze mammograms and for early lung cancer screening. Doctors evaluating mammograms for breast cancer miss 40% of cancers, and ML can improve on that figure.
ML is also trained and used to classify tumors, find bone fractures that are hard to see with the human eye and detect neurological disorders.
The discovery and manufacturing of new medications, which traditionally go through involved, expensive and time-consuming tests, can be sped up using ML.
Machine learning even powers a lot of what happens with our smartphones. For instance, ML algorithms govern the facial recognition we rely on to turn on our phones. They also power the voice assistants that set alarms and compose messages. These include Apple’s Siri, Amazon’s Alexa, Google Assistant and Microsoft’s Cortana, which use NLP to recognize what we say and respond appropriately.
Want to learn more? Tonex offers Machine Learning Training a 3-day technical training course covering the fundamentals of machine learning and much more.
Participants learn differences and similarities between machine learning, artificial intelligence, deep learning, data mining and data warehouse. Artificial intelligence uses models built by machine learning to create intelligent behavior applied to businesses, marketing and sales, operations, autonomous cars, games and industrial automation by prediction based on rules and using programming languages and algorithms.
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