Exploring Topics in Data Science

Curious about sentiment analysis in Python? Want to learn about Naive Bayes? Explore these topics and more in our Exploring Topics in Data Science course.

In this course,you'll learn concepts such as the Naive Bayes theorem, Naive Bayes classifiers, and the K-Nearest Neighbors algorithm (KNN). You'll also learn the concept of Euclidean distance and how it plays a role in the kNN algorithm, and how to evaluate the mean squared error for predictions that are the kNN algorithm predicts.

You’ll use Naive Bayes classifiers — they figure out how likely data attributes are associated with a certain class — to classify movie reviews based on sentiment, which means you’ll be doing some sentiment analysis.

You’ll also the K-Nearest Neighbors algorithm to identify the NBA player who's the most similar to Lebron James. The K-Nearest Neighbors algorithm is based on the idea that we can predict values we don't know by matching them with the most similar values we do know.

Additionally, this course covers how to compute prediction error using the receiver operating characteristic curve, which tells you how good a model is. Computing error is a very important measure of your model, because it tells you whether your model is getting better or worse.

By the end of this course, you'll be able to:

  • Learn how to determine if text is described as a positive or negative sentiment.
  • Understand the K-Nearest neighbors algorithm.

Learn by coding!

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Explore Topics in Data Science

Naive Bayes for Sentiment Analysis

Learn how to use Naive Bayes to classify movie reviews based on sentiment.

An Introduction to K-Nearest Neighbors

Use the kNN algorithm to identify the NBA player who's the most similar to Lebron James.

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