Learn how to use the Naïve Bayes method.
- [Instructor] Naive Bayes classification…is a machine learning method that you can use…to predict the likelihood that an event will occur…given evidence that's supported in a dataset.…For the demo in this segment,…we're going to build a Naive Bayes classifier…from our large dataset of emails called spam base.…Some of the records in the dataset are marked as spam…and all of the other records are marked as not spam.…The predictive features in this dataset…serve as our evidence.…Using them, we can build a spam filtering system…with a Naive Bayes model…and successfully predict which incoming emails are spam…and which are not.…
Naive Bayes is a machine learning method…that you can use to predict the likelihood…that an event will occur…given evidence that's present in your data.…This is also called conditional probability…in the world of statistics.…There are three types of Naive Bayes models.…There's our multinomial, Bernoulli, and Gaussian.…Multinomial is good for when your features are…categorical or continuous…and describe discrete frequency counts.…
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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