中Internet of things (IoT) has become a significant component of urban life, giving rise to “smart cities.” These smart cities aim to transform present-day urban conglomerates into citizen-friendly and environmentally sustainable living spaces. The digital infrastructure of smart cities generates a huge amount of data that could help us better understand operations and other significant aspects of city life.
Data Mining for Smart Cities
您将学到什么
Describe types of smart city-generated datasets, data mining techniques, and how to implement them using Python 3.
Explain how to read and preprocess data for data mining.
Apply data mining techniques to smart city-generated data and visualize and interpret the physical implications of the results.
您将获得的技能
- Data Mining
- Unsupervised Learning
- Deep Learning
- Pandas (Python Package)
- Big Data
- Artificial Neural Networks
- Data Processing
- Anomaly Detection
- Data Cleansing
- Python Programming
- Scikit Learn (Machine Learning Library)
- Exploratory Data Analysis
- Data Science
- Data Visualization
- Data Manipulation
- Regression Analysis
- Machine Learning Algorithms
- Supervised Learning
要了解的详细信息
可分享的证书
添加到您的领英档案
作业
12 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
该课程共有8个模块
This module provides an overview of the course content and structure. In this module, you will learn about the different course elements. In this module, you will get acquainted with your instructor and get an opportunity to introduce yourself and interact with your peers.
涵盖的内容
2个视频1篇阅读材料1个讨论话题
In this module, you will learn about data mining, why we need it, and the approach. The module also presents the basics of probability and statistics, which form the foundation for data mining. You will also gain insight into data preprocessing and data mining task identification.
涵盖的内容
12个视频4篇阅读材料2个作业1个讨论话题
In this module, you will learn about Python programming for data mining. The module also discusses important Python modules: NumPy , SciPy, and Matplotlib. You will learn to install Python using Anaconda and use the Jupyter notebook to write your code. The module also presents some examples demonstrating data preprocessing using Python.
涵盖的内容
6个视频4篇阅读材料2个作业3个非评分实验室
In this module, you will learn about supervised learning (learning from examples). The module discusses two supervised learning tasks: regression and classification. You will also gain insights into several classification algorithms such as Bayesian classifiers, decision trees, support vector machines (SVM), and ensemble classifiers.
涵盖的内容
12个视频5篇阅读材料2个作业1个讨论话题9个非评分实验室
In this module, you will learn about unsupervised learning (learning from unlabelled data without any ground truth labels). The module also discusses frequent itemset mining. You will also gain an insight into several data clustering algorithms such as distribution-based, partitional, and hierarchical clustering.
涵盖的内容
11个视频5篇阅读材料2个作业1个讨论话题7个非评分实验室
In this module, you will learn about anomaly detection problems and algorithms. You will gain insight into anomaly detection techniques. You will learn to validate your results. When applying data mining to smart city data, you will also learn to avoid false discoveries using statistical significance testing and hypothesis testing.
涵盖的内容
5个视频2篇阅读材料2个作业4个非评分实验室
In this module, you will learn about some advanced data mining algorithms such as artificial neural networks (ANN) and deep learning. You will develop an understanding of the applications of these algorithms. The module also analyzes hidden Markov models (HMMs) for modeling time series (sequential) data.
涵盖的内容
10个视频3篇阅读材料1个作业1个讨论话题4个非评分实验室
In this module, you are provided with your term-end project, instructions to complete the project, and the criteria for how your instructor will grade your submission.
涵盖的内容
1个视频2篇阅读材料1个作业1个非评分实验室1个插件
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