What Is Machine Learning (ML)

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

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. E.g., ML algorithms can identify examples, experiences, instructions, etc. 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, 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.

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