You are correct that Soft Computing and Machine Learning are related fields, but they are not interchangeable terms. Soft Computing is a subfield of Artificial Intelligence that focuses on the development of algorithms and techniques that can handle imprecision, uncertainty, and partial truth. In contrast, Machine Learning is a subfield of Artificial Intelligence that focuses on the development of algorithms that can learn from data and make predictions or decisions based on that learning.

As explained in this [source](https://www.tutorialspoint.com/difference-between-ai-and-soft-computing#:~:text=AI%20aims%20at%20making%20machines,deals%20with%20imprecision%20and%20probabilities.&text=AI%20needs%20the%20appropriate%20data,handle%20ambiguous%20and%20noisy%20data.), Soft Computing includes techniques such as fuzzy logic, neural networks, and genetic algorithms, while Machine Learning includes techniques such as supervised learning, unsupervised learning, and reinforcement learning. While there is some overlap between the two fields, the primary difference is that Soft Computing is concerned with handling uncertainty and imprecision, whereas Machine Learning is concerned with learning from data.

It's worth noting that some techniques from Soft Computing, such as neural networks, are commonly used in Machine Learning. Similarly, some techniques from Machine Learning, such as clustering algorithms, can be used in Soft Computing. Therefore, while the two fields have distinct focuses, there is some overlap and interchangeability between them.
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