- [Voiceover] One of the more interesting…methods or algorithms in machine learning…is artificial neural networks.…Now that name may suggest that we're trying…to build a brain, but what we're really…trying to do is develop algorithms…that follow the brain's lead.…In particular artificial neural networks…are inspired by biological neural networks,…and they have data neurons or really algorithmic neurons…that exchange information between each other…with connections that adapt or weights…that change according to experience.…
Some common uses of neural networks are…in situations where rule-based learning doesn't work,…it doesn't follow through, you can't…describe it well enough.…This includes for instance, computer vision,…trying to recognize photos and videos,…speech recognition, and really any time…you're trying to infer a nonlinear function…that's difficult to describe.…What we have here is sort of a black box model…where you have input, it goes into the black box,…there's some sort of hidden processing there,…here it's with the neural network,…
- 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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