Module 3 - Machine Learning Deep Dive
Machines learn in different ways and there are several different strategies or methods to apply for a use case in the form of supervised, unsupervised, semi-supervised, and reinforcement learning. Training a machine using basic rules or letting the machine discover patterns independently or using a mix of the two will cover many different problems that can be addressed in an organization. This module will be a mixture of deep-dive into machine learning types, limitations, and hands-on development of machine learning solutions.
- Select the appropriate machine learning task for a real-world application
- Use a dataset to fit a new model
- Build a machine learning model based on the business application
- Use cross-validation for a supervised learning model
- Assess the model performance in terms by error metrics for each ML task.
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