Machine learning may remind you of science fiction movies and books that depict a future with sentient machines capable of interacting with humans and performing tasks just like people would. But what if we told you that this future is already a reality? In fact, all of this is already possible!
What is machine learning?
Machine learning (ML) is a science of programming machines to think and act like humans without being specifically programmed to. We already use ML in our daily life without knowing it. Email, spam, recognition, spell check, even the YouTube video recommendation which brought you here are implemented using ML.
How does it work?
Machine learning uses algorithms to learn tasks. These algorithms are fed with data from which they learn to perform these tasks. This means that over time, as changes in data occur, we don’t need to reprogram our application, just let it find patterns and learn from the new data.
Machine learning and artificial intelligence
Machine learning is a subset of artificial intelligence, which is a science concerned with imparting humanlike intelligence onto machines and creating machines which can sense, reason, act and adapt. Deep learning is a subbranch of ML which is inspired by the working of the human brain.
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10 Everyday Examples of Machine Learning
- Spam filtering: This is a classic example. Email providers use ML to identify spam emails and block them from reaching your inbox.
- Fraud detection: Banks and other financial institutions use ML to detect fraudulent transactions.
- Image recognition: This is a rapidly growing field of machine learning. ML algorithms can be used to identify objects in images, such as faces, cars, and buildings.
- Speech recognition: This is another rapidly growing field of machine learning. ML algorithms can be used to transcribe speech into text.
- Natural language processing: This is a field of computer science that deals with the interaction between computers and human (natural) languages. ML algorithms can be used to perform tasks such as text translation, sentiment analysis, and question answering.
- Recommendation systems: These systems are used to recommend products, movies, and other items to users. ML algorithms are used to learn the preferences of users and recommend items that they are likely to be interested in.
- Self-driving cars: Self-driving cars use a variety of ML algorithms to navigate the road, avoid obstacles, and make decisions.
- Medical diagnosis: ML algorithms can be used to diagnose diseases and recommend treatments.
- Climate change research: ML algorithms can be used to analyze climate data and predict future climate trends.
- Financial trading: ML algorithms can be used to predict stock prices and other financial trends.
You may have questions now “How machines can learn?” 🤨 Check the next post about THIS!