ML covers foundational concepts like supervised, unsupervised, and reinforcement learning and algorithms such as linear regression, decision trees, neural networks, and clustering. A significant part of ML involves data cleaning, pre-processing, and selecting relevant features that improve the accuracy and efficiency of models. Learning ML includes techniques for evaluating model performance (using metrics like accuracy, precision, and recall) and optimizing models to generalize new data well.
- Teacher: ks arunima