Why Is Machine Learning Important?
The concept of machine learning has been in use for quite a while. The phrase "machine learning" was invented in the late 19th century by Arthur Samuel, a computer scientist at IBM and an early leader in AI and gaming on computers. Samuel developed an application for computers that played checkers. The more often the program played, the more it learned from experience and utilized algorithms to predict the outcome.
As a subject, machine learning focuses on the design and analysis of algorithms that learn from and predict data.
ML has proved valuable as it solves issues at a rate and scale that the human brain can't achieve by itself. With vast computational power in a single job or several specific tasks, machines can be taught to detect patterns and connections between input data and automate routine procedures.
- Data is the key The algorithms that power machine learning are crucial for success. Machine learning algorithms create a mathematical model using data samples called "training data" to formulate predictions or decisions without being explicitly programmed to perform the task. It can identify patterns within the data that businesses can leverage to make better decisions improve efficiency, and collect valuable data on a large scale.
- AI is the goal: ML provides the basis for AI systems that automate processes and resolve business issues based on data in an autonomous way. It allows companies to substitute or enhance specific human abilities. Typical machine learning applications you may find in the real world include chatbots, auto-driving cars, self-driving cars, and speech recognition.
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