What Is Machine Learning (ML)

Are you interested in knowing what is Machine Learning?

Machine Learning is a capability within the overarching umbrella of “Artificial Intelligence” (AI). Check out some pros and cons of AI technology in our blog post.

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Organizations use ML data science to create computer systems that can “learn” without explicit programming for “learning.” How do these computer systems learn then? Well, they learn to identify data points from very large pools of data.

ML or AI solutions utilize computer algorithms that study and analyze this training data. Moreover, these algorithms can observe patterns. For example, ML algorithms can identify examples, experiences, instructions, and so on in the data sets.

The basic design of AI and ML means that you can use it for pretty much anything. Depending on the training set and the input data, the AI predictor or system can learn just about anything. This AI tutorial explains this in depth.

After being trained by data scientists, these algorithms can then go on to make decisions and predictions. As we feed larger data sets to them, they learn to perform their tasks better and better, thereby boosting performance. You can read our guide “Machine Learning in future software development” for more deep learning insights.

Read more about "What is machine learning?" here and here.

FAQ

What is the difference between ML and AI?

ML is a subset of AI that is focused on making predictions based on its ability to learn from numerous different data sets.

Can an ML system think?

While not comparable to the complexity of human thought which includes an idea of the self and consciousness, ML systems are able to learn to undertake and provide a reliable perspective on many tasks.

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What are some uses of ML?

Everything from chatbots to supply chain automation are examples of current ML uses.

How machine learning works?

Machine learning is based on mathematical logic. It involves training algorithms on data sets to achieve an expected result, such as identifying a pattern or recognizing an object.

What are types of machine learning?

1. Supervised Learning: A model is trained on a dataset containing both input and desired output (labeled data) to establish a correspondence between them.
2. Unsupervised Learning: A model is trained on unlabeled data, identifying inherent patterns, groupings, or structures without any prior output information.
3. Reinforcement Learning: An agent learns by interacting with the environment, using a system of rewards and penalties to determine the best actions.