In this course we continue with discussing machine learning models and algorithms. While the focus in Introduction to Machine Learning 1 was on programming basic models yourself, in this course we will make more use of libraries for the elementary parts and the focus will mainly be on how to combine these parts into more complex models. You will apply these more complex models in your own code to real data sets, where we will also cover common operations for such data.
Specifically, we’ll cover the following topics: Logistic Regression; Feedforward Neural Networks and Backpropagation; ReLU, Softmax, Dropout and Batchnorm layers; Decision Trees and Random Forests; Convolutional Neural Networks