Learn about a linear projection method for multivariate data.
- [Narrator] DBSCAN is an unsupervised…machine-learning method that clusters…core samples from dense areas of…a dataset and denotes non-core…samples from sparse areas of that dataset.…An example of where you would use DBSCAN is…imagine you're working on a computer vision…project for the advancement of self-driving cars.…You've got some line data that's…supposed to represent lanes, but you need…to be able to predict lanes from lines.…You can use DBSCAN to predict the…lanes based on the density of the…lines or non-density of the lines.…
Where dense areas are clustered…into core samples, which will be…considered lanes, and non-dense areas…or sparse areas will be considered…non-core samples, non-drivable areas.…This way the car knows where to go.…You can use DBSCAN to identify collective outliers.…Just make sure that the number of outliers…you choose is less than 5% of the total…number of observations in your dataset…You do that by adjusting your model parameters accordingly.…Then two important model parameters…for DBSCAN are eps and min_samples.…
AuthorLillian Pierson, P.E.
- Getting started with Jupyter Notebooks
- Visualizing data: basic charts, time series, and statistical plots
- Preparing for analysis: treating missing values and data transformation
- Data analysis basics: arithmetic, summary statistics, and correlation analysis
- Outlier analysis: univariate, multivariate, and linear projection methods
- Introduction to machine learning
- Basic machine learning methods: linear and logistic regression, Naïve Bayes
- Reducing dataset dimensionality with PCA
- Clustering and classification: k-means, hierarchical, and k-NN
- Simulating a social network with NetworkX
- Creating Plot.ly charts
- Scraping the web with Beautiful Soup
Skill Level Beginner
1. Data Munging Basics
2. Data Visualization Basics
3. Basic Math and Statistics
4. Dimensionality Reduction
Explanatory factor analysis6m 39s
5. Outlier Analysis
6. Cluster Analysis
7. Network Analysis with NetworkX
8. Basic Algorithmic Learning
9. Web-based Data Visualizations with Plotly
10. Web Scraping with Beautiful Soup
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