Neural Networks and Random Forests

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Overview

This course delves into two powerful machine learning algorithms: neural networks and random forests. You will explore the fundamental concepts behind neural networks, including perceptrons, multilayer architectures, activation functions, and backpropagation, which enable deep learning models to recognize complex patterns in data. The course also covers random forests, an ensemble learning method that builds multiple decision trees and merges their results for improved accuracy and robustness. You will learn how random forests handle overfitting and manage high-dimensional data effectively. Practical...

Syllabus

Introduction to Neural Networks

In this module, we'll go through neural networks and how to use them in Python. We'll start by describing what a neural network is and how to construct one by combining a sequence of linear models. Then, we'll talk about converge of neural networks in the hopes of minimizing a loss function. Finally, we'll learn how to code a neural network in Python.

Deep Dive into Neural Networks

In this module, we'll take a more detailed look into neural network and the considerations we should be having when using them. We'll start by adding layers to our 2-layer network, exploring the different options and their effects. Then, we'll explore some more advanced Python libraries for neural networks in TensorFlow and Keras. Finally, we'll discuss the implications to science and how to apply the models in the space.

Exploring Random Forests

In this module, we'll build up our knowledge of random forests and their uses in science. We'll start by exploring decision trees and how they operate as models in isolation. Next, we'll look at the impact of combining decision trees to create random forests. From here, we'll talk about the similarities and differences between regression and classification with random forests before concluding with a final project predicting species from lineage.