DS 201-2 - Building and Evaluating ML Models
In DS 201-2, we will learn how to build your own Machine Learning models, We will be using the popular SciKit Learn and TensorFlow libraries to show you how Data Scientists “learn” a model to a dataset and how to make predictions with those models. We will also be covering the finer points of ML Modeling, namely Data Prep, Parameter Selection, and testing Model performance.
Working with Categorical Data
Regularization and Normalization
Support Vector Machines (SVMs)
Neural Networks (Introduction)
Will encompass the entire data science project pipeline: data acquisition, data exploration, data analysis, model building, model evaluation.