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When labeled data is available for prediction tasks like spam filtering, stock price forecasting Machine learning (ml) is the enabling technology for intelligent automation, predictive analytics, and network optimization in contemporary telecom systems. When exploring data structures without predefined labels like customer segmentation, anomaly detection.

When you decide to start the journey into machine learning, there are three main types of machine learning you should know Supervised, unsupervised & reinforcement learning Supervised learning, unsupervised learning, and reinforcement learning

Below, we cover each of these different approaches in depth.

At the core of ml are three main paradigms Each of these paradigms solves different kinds of problems, uses different data types, and has its own set of algorithms, strengths, and limitations. In the expanding field of machine learning (ml), three primary types of learning paradigms stand out Reinforcement learning is like teaching a child to ride a bike

They learn by trying, falling, and improving each time, with rewards for doing better In this approach, an agent interacts with. Explore the core differences between supervised, unsupervised, and reinforcement learning in machine learning, including their methods, data requirements, and common applications. Supervised learning happens in the presence of a supervisor just like learning performed by a small child with the help of his teacher

As a child is trained to recognize fruits, colors, and numbers under the supervision of a teacher this method is supervised learning.

Broad classification of machine learning

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