- [Voiceover] The first procedure in machine learning that…we want to talk about is decision trees.…It reminds me of the bumper sticker that says…trees are the answer and in machine learning they often are.…This is an example of a very simple decision tree.…Let's take a quick look at the anatomy of this chart.…You haven't called a root node, that's the starting point,…it's at the top, then you start having these splits…which are indicated on branches…or edges is the mathematical term.…You have nodes which are decision points.…
And then you finish with the leaves or the terminal nodes…which are the last category that your data ends up in.…Now, decision trees come in two general categories.…There are classification trees, which use quantitative…and categorical data model categorical outcomes,…and there are regression trees, that use the same kind…of quantitative and categorical data,…except this time to model quantitative outcomes.…Sort of like multiple regression…but trees are often easier to set up and interpret.…
- 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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