Understanding and extracting hidden patterns or features from the data is the learning process in machine learning. Instead of using explicit logic supplied by people, machine learning has the capacity to learn from experiences. Conventional systems are created with the use of well defined human-set rules. In order for machine learning algorithms to understand complicated patterns from inputs (x), they use outputs (y) as a feedback signal. Thus, an intelligent program is the ML system's final product.
We often use a logical method to solve any issue. We make an effort to break the task up into several smaller tasks and solve each smaller task using a distinct rationale. When dealing with extremely complicated jobs, like stock price prediction, the patterns are always changing, which has an impact on the results. That implies that, in order to answer this problem logically, we must adjust our handwritten logic for each new change in the outputs. Machine Learning (ML), on the other hand, creates the model using a vast amount of data. The data gives the model all of its historical experience, which helps it better understand the pattern. We just retrain the model with fresh instances whenever the data changes.