Predicting Extreme Climate Behavior with Machine Learning

Predicting Extreme Climate Behavior with Machine Learning

本课程是 Modeling and Predicting Climate Anomalies 专项课程的一部分

位教师：Osita Onyejekwe

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5个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

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

您将获得的技能

您将学习的工具

Classification Algorithms

要了解的详细信息

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作业

4 项作业

授课语言：英语（English）

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积累特定领域的专业知识

本课程是 Modeling and Predicting Climate Anomalies 专项课程专项课程的一部分

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该课程共有5个模块

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.

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://hua.dididi.sbs/degrees/master-of-science-data-science-boulder

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

涵盖的内容

6个视频4篇阅读材料1个作业1个编程作业1个讨论话题1个非评分实验室

6个视频总计61分钟

Introduction to the Course 5分钟
Meet the Instructor 1分钟
Introduction to Unsupervised Learning and Techniques 5分钟
PCA Overview 19分钟
PCA in Terms of SVD 23分钟
PCA on Soil Temperature Data: Notebook Walkthrough 7分钟

4篇阅读材料总计51分钟

Course Updates and Accessibility Support 1分钟
Earn Academic Credit for your Work! 10分钟
Course Support 10分钟
Principal Component Analysis for Extremes and Application to U.S. Precipitation 30分钟

1个作业总计20分钟

Principal Component Analysis and Singular Value Decomposition (SVD) 20分钟

1个编程作业总计60分钟

PCA on Soil Moisture Data 60分钟

1个讨论话题总计30分钟

Unsupervised Learning and Climate Anomalies 30分钟

1个非评分实验室总计30分钟

PCA on Soil Temperature Data: Notebook Walkthrough 30分钟

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.

涵盖的内容

3个视频4篇阅读材料1个作业1个编程作业1个非评分实验室

3个视频总计38分钟

Introduction to K-Means Clustering 13分钟
K-Means Clustering Mathematical 15分钟
What is Clustering: Notebook Walkthrough 11分钟

4篇阅读材料总计125分钟

K-Means Clustering Theoretical 30分钟
K-Means Clustering Extension 45分钟
Cluster Analysis 30分钟
Clustering and Trend Analysis of Global Extreme Droughts from 1900 to 2014 20分钟

1个作业总计30分钟

K-Means Clustering 30分钟

1个编程作业总计60分钟

Clustering 60分钟

1个非评分实验室总计30分钟

What is Clustering: Notebook Walkthrough 30分钟

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.

涵盖的内容

2个视频2篇阅读材料1个作业1个编程作业2个非评分实验室

2个视频总计21分钟

Introduction to Statistical Regression: Notebook Walkthrough 8分钟
Introduction to Multiple Linear Regression: Notebook Walkthrough 12分钟

2篇阅读材料总计45分钟

Linear Regression 25分钟
Prediction of Climate Variable using Multiple Linear Regression 20分钟

1个作业总计10分钟

Linear Regression 10分钟

1个编程作业总计60分钟

Linear and Multiple Linear Regression 60分钟

2个非评分实验室总计60分钟

Introduction to Statistical Regression: Notebook Walkthrough 30分钟
Introduction to Multiple Linear Regression: Notebook Walkthrough 30分钟

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.

涵盖的内容

9个视频3篇阅读材料3个编程作业2个非评分实验室

9个视频总计128分钟

Introduction to Logistic Regression: Theoretical 20分钟
Introduction to Logistic Regression: Notebook Walkthrough 11分钟
Introduction to Decision Trees: Theoretical 14分钟
Introduction to Decision Trees: Practical Notebook Walkthrough 14分钟
Support Vector Machines: Part 1 15分钟
Support Vector Machines: Part 2 7分钟
Support Vector Machines: Part 3 14分钟
Support Vector Machines: Part 4 23分钟
Introduction to Support Vector Machines: Practical Notebook Walkthrough 10分钟

3篇阅读材料总计100分钟

Logistic Regression 20分钟
Decision Trees and Random Forests 20分钟
A Guide to Support Vector Machines and Tutorial 60分钟

3个编程作业总计180分钟

Logistic Regression 60分钟
Decision Trees 60分钟
Support Vector Machines 60分钟

2个非评分实验室总计60分钟

Introduction to Decision Trees: Practical Notebook Walkthrough 30分钟
Introduction to Support Vector Machines: Practical Notebook Walkthrough 30分钟

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.