Introduction to Model Design
Explore the fundamental process of designing machine learning models from start to finish. This lesson guides you through key stages such as importing libraries and datasets, performing exploratory data analysis and data scrubbing, splitting data for validation, setting algorithms, making predictions, and evaluating and optimizing your model. By understanding this structured approach, you will be able to confidently create and refine your own machine learning workflows.
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Quick overview of model design
Before exploring specific supervised learning algorithms, it’s useful to pause and take a high-level look at the full procedure of building a machine learning model. This involves reviewing several steps examined in the preceding chapters and introducing new methods, including evaluating and predicting.
These 10 steps take place inside your development environment and follow a relatively fixed sequence. Once you are familiar with this framework, you will find it easy to design your own machine learning models from start to finish.
The following steps are involved in the model design: ...