Throughout Predicting Extreme Climate Behavior with Machine Learning, you'll explore both theoretical concepts and practical applications or machine learning and data analysis. You'll begin by analyzing unsupervised learning algorithms, mastering techniques like clustering and dimensionality reduction, and applying them to real-world climate datasets. You'll also explore supervised learning, gaining hands-on experience with algorithms such as Logistic Regression, Decision Trees, and Neural Networks.
Empfohlene Erfahrung
Was Sie lernen werden
Analyze and differentiate between various machine learning algorithms, including unsupervised and supervised methods
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
Kompetenzen, die Sie erwerben
- Kategorie: Unsupervised Learning
- Kategorie: Climate Modeling
- Kategorie: Supervised Learning
- Kategorie: Statistical Analysis
- Kategorie: Machine Learning
Wichtige Details
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August 2024
4 Aufgaben
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In diesem Kurs gibt es 5 Module
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
Das ist alles enthalten
6 Videos3 Lektüren1 Aufgabe1 Programmieraufgabe1 Diskussionsthema1 Unbewertetes Labor
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.
Das ist alles enthalten
3 Videos4 Lektüren1 Aufgabe1 Programmieraufgabe1 Unbewertetes Labor
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.
Das ist alles enthalten
2 Videos2 Lektüren1 Aufgabe1 Programmieraufgabe2 Unbewertete Labore
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.
Das ist alles enthalten
9 Videos3 Lektüren3 Programmieraufgaben2 Unbewertete Labore
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.
Das ist alles enthalten
3 Videos4 Lektüren1 Aufgabe1 Diskussionsthema1 Unbewertetes Labor
Dozent
Empfohlen, wenn Sie sich für Data Analysis interessieren
University of Colorado Boulder
University of Colorado Boulder
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