- [Voiceover] One of the most important developments…in machine learning is the use of Ensembles,…or Ensemble modeling.…You can think of this as the statistical version…of the Wisdom of the crowd.…The idea here is that you're going to…combining estimates, that is,…the average of many different estimates…of your particular outcome.…The reason you wanna do this is because…the combined estimates are often more accurate…than any one individual estimate.…So for instance, in contests where people…are asked to guess the number of marbles in a jar,…if you get the average of everybody's estimate,…'cause some people overestimate, some underestimate,…that average is usually closer to the…true number than any one person's guess.…
Now, when Combining Estimates, there's…one other thing to remember.…Diversity helps, and I'll explain…a little more about that.…There are a few different methods of Combining Estimates.…Three of the most common are, first, Bagging,…which is short for boosted aggregating.…This is where you use randomly drawn datasets.…
- 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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