Data can be viewed in higher and lower dimensions, and this module will help you explore this key aspect of data science. PCA/SVD are two key methods of unsupervised machine learning in terms of dimensional reduction

In this module, we delve into the concept of clustering, a fundamental technique in data analysis and machine learning. Clustering involves grouping a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. This module will provide a comprehensive exploration of clustering, including its various derivations, such as hierarchical clustering and K-Means.

Regression is a cornerstone technique in machine learning, particularly when working with continuous variables, and is essential for modeling relationships between variables and predicting outcomes. In this module, we will explore the fundamental principles of regression, focusing on linear regression.

In this module, we will explore classification techniques, a critical aspect of supervised learning in machine learning. Classification is the process of assigning labels to input data based on its features, and it is widely used for tasks like spam detection, medical diagnosis, and image recognition. Throughout this module, we will explore several key classification methods, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM). Each of these techniques offers unique strengths and is suited to different types of data and problem contexts. By the end of this module, you will have a thorough understanding of how these classification algorithms work, how to implement them, and how to choose the right method for your specific supervised learning challenges.

This final module dives into Neural Networks and its application to climate data, primarily with different activation functions, layers, neurons and architectural structures of the network.