By Vaishnavi Dorik
The Machine Learning process includes several steps used to build a model that can learn from data and make predictions. The first step is Data Collection, where relevant data is gathered from various sources such as databases, websites, sensors, or surveys. This data is used to train the machine learning model, and the quality of the data greatly affects the accuracy of the system. The next step is Data Preprocessing, where the collected data is cleaned and prepared before training the model. This process involves removing missing values, correcting errors, and converting the data into a suitable format so that the model can learn patterns effectively and produce accurate results.
Machine Learning Unit 1.pdf
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