• Specialization:听Climate Change Anomalies
  • Instructor:听Osita Onyejekwe
  • Prior knowledge needed:听Intro to R programming

Learning Outcomes

  • Analyze and differentiate between various machine learning algorithms, including unsupervised and supervised methods 听
  • Apply dimensionality reduction techniques, such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), to complex datasets
  • Implement supervised learning algorithms using Python, and evaluate their performance through practical exercises and real-world case studies.
  • Develop and apply effective clustering methods to analyze and segment data 听

Course Content

Duration: 5h

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听

Duration: 2h

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.

Duration: 3h

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.

Duration: 1h30min

You will complete a peer reviewed final project worth 20% of your grade. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.

Note: This page is periodically updated. Course information on the Coursera platform supersedes the information on this page. Click View on Coursera button听above for the most up-to-date information.