Join Dan Sullivan for an in-depth discussion in this video Building models, part of Advanced NoSQL for Data Science.
- [Instructor] The practice of machine learning…is the process of building and evaluating models.…Well, what's a model?…It's a computational procedure…for making a specific prediction or calculating a value,…and the details of that procedure…are learned by analyzing data.…Now, building a model begins with training.…Training data is often in a tabular structure.…The other part of training is a machine learning algorithm.…And there are two main types of machine learning algorithms,…supervised and unsupervised.…Supervised learning uses examples,…such as customers who respond to e-mail campaigns…and those who don't.…
Now, unsupervised learning does not have positive…and negative examples like that.…So clustering is a type of unsupervised learning,…and it's used for data exploration.…In this lesson, however,…we'll focus just on supervised learning.…Classification algorithms are used…to categorize entities into groups,…such as customers that respond to campaigns,…vehicles likely to break down soon,…or students who will respond to a new teaching method.…
The course begins with an introduction to NoSQL, and then delves into the specifics of document, wide-column, and graph databases. Learn key details for performing data preparation, exploration, and extraction for each type of NoSQL database. Review case studies that show how to use various NoSQL databases with popular data science tools, including the document database MongoDB, the wide-column database Cassandra, and the graph database Neo4j.
- NoSQL compared to traditional relational databases
- Performing common data science tasks
- Preparing data with document databases
- Manipulating data in NoSQL
- Preparing, exploring, extracting, and model building
- Working with document, wide-column, and graph databases
- Reviewing case studies using MongoDB, Cassandra, and Neo4j
Skill Level Advanced
1. Why NoSQL?
Types of NoSQL databases2m 20s
2. Perform Common Data Science Tasks with NoSQL Databases
3. Document Databases for Data Science
4. Wide-Column Databases for Data Science
5. Graph Databases for Data Science
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