- [Voiceover] Another common choice…in classifying algorithms is the Naive Bayes classifiers.…Now, you can think of this as…the unreasonable effectiveness of naivete,…or of simple solutions.…The term "Naive Bayes" requires a little explanation.…It's a classification method,…so it's trying to take the cases in your data set…and place them into categories.…It's "Bayes," or "Bayesian,"…because it uses Bayes' theorem,…where it's trying to get the probability…of the class that the data should be in,…given the data.…
And it's "naive" because it ignores…the relationships between predictors.…Now that might seem like a problem,…but one of the interesting things is…it's very effective nonetheless.…Despite the naivete,…or ignoring the relationships between variables,…Naive Bayes still works very well,…and in fact it works even better with data preparation.…That can include things like balancing class size.…It can include normalizing the classification weights…to compensate for dependence among features.…It can include transforming the data,…
- Assess the skills required for a career in data science.
- Evaluate different sources of data, including metrics and APIs.
- Explore data through graphs and statistics.
- Discover how data scientists use programming languages such as R, Python, and SQL.
- Assess the role of mathematics, such as algebra, in data science.
- Assess the role of applied statistics, such as confidence intervals, in data science.
- Assess the role of machine learning, such as artificial neural networks, in data science.
- Define the components of effective data visualization.
Skill Level Beginner
1. What Is Data Science?
2. Fields of Study
Ethical issues2m 39s
4. Data Sources
5. Data Exploration
8. Applied Statistics
9. Machine Learning
Next steps2m 17s
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