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Updated on: Sep 23, 2023
There are 5 different types of AI (Artificial Intelligence) and Machine Learning that are used in the industry.
AI is a set of algorithms and technologies that are based on an approach which is called machine learning.
This approach to solving problems through analysis and pattern recognition has been around for a long time, but it’s only now that we have enough processing power to make it work in complex environments like games or self-driving cars.
The first type of artificial intelligence is called statistical inference. It’s also considered a subset of machine learning and one of the simplest forms of artificial intelligence. This type of AI uses statistics, probability and random processes to solve problems in its environment. A classic example would be spam filters, which use statistical inference to identify what messages are spam by looking at which messages get flagged most often by other users who have installed the same spam filter software. Statistical inference can be applied to all sorts of problems, from preventing credit card fraud to identifying objects
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AI and ML are two terms that sound almost the same and are often used interchangeably. However, there is a difference between AI vs ML in the world of technology, which is what we will be exploring in this section.
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Differentiating between artificial intelligence (AI) and machine learning (ML) might seem like a difficult task. But if you look closely, they have many different aspects that differ from each other. They both work to create a more efficient future for us. And the only way to understand how they are different is by understanding their individual definitions.
AI is definitely the future. Machine learning, being the current application of artificial intelligence, is based on the idea to give computer access to data and make them learn themselves. There are obviously various ways to start.
There are two broad perspectives on getting into AI and machine learning; first, the API and second, the algorithms. These two prospects are hardly covered when you start an online course or you read a book. The best way to gain knowledge is by executing it in practical ways. Various Machine learning classes in Pune have been opened since.
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Some recommendations to start from:
Bayesian Reasoning and Machine learning by David Barber
Machine learning: A probabilistic perspective by Kevin Murphy
Data science from scratch: First-principles with Python by Joel Grubs’
MOOCs (Massive open online courses)
Andrew Ng Machine learning at Coursera
Sebastian and Katie at Udacity