Case Study Description: To predict the trip duration of a New York taxi cab ride, we can build different types of features and evaluate them. We will start by describing what is a feature in this context; then we will develop some elementary features and add features using the software package featuretools. We will assess how these features perform in predicting trip duration.
The idea of this notebook is to explore a step-by-step approach to create a single layer neural network without the help of any third party library. In practice, this neural network should be useful enough to generate a simple non-linear regression model, though it’s final goal is to help us understand the inner workings of one.
The idea of this notebook is to explore a step-by-step approach to create a Linear Classifier with Softmax without the help of any third party library. We will later compare its results with two other supervised learning algorithms such as Neural Network and K-Nearest Neighbors in order to see if there’s any difference in performance and accuracy.
In practice, these Algorithms should be useful enough for us to classify our data whenever we have already made clusters (in this case color) which will serve as a starting point to train our models.
The idea of this notebook is to explore a step-by-step approach to create a K-Nearest Neighbors Algorithm without the help of any third party library. In practice, this Algorithm should be useful enough for us to classify our data whenever we have already made clusters (in this case color) which will serve as a starting point to find neighbors.
