Advanced Classification
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What’s Included
Course Introduction
Course Introduction
In this lesson, we get a preview of the course ahead.
Understanding Bayes Theorem
Understanding Bayes Theorem
The building block of Naive Bayes algorithms is a simple but effective formula called Baye's Theorem. This theorem is a form of conditional probability that happens to be very useful in machine learning applications.
Understanding Multinomial Naive Bayes
Understanding Multinomial Naive Bayes
The Multinomial Naive Bayes algorithm is a specific use case of Bayes theorem that's best suited for text classification.
Preparing Multinomial Naive Bayes Model
Preparing Multinomial Naive Bayes Model
Getting data ready for classification algorithms can take a few steps. Learn how to create dummy features and split data into training and testing versions.
Building a Multinomial Naive Bayes Model
Building a Multinomial Naive Bayes Model
Use 3 different functions to manipulate your data and train a Multinomial Naive Bayes model in just 2 lines of code.
Evaluating a Multinomial Naive Bayes Model
Evaluating a Multinomial Naive Bayes Model
Use a confusion matrix heatmap and classification report to evaluate the performance of a Multinomial Naive Bayes model.
Understanding Gaussian Naive Bayes
Understanding Gaussian Naive Bayes
Learn how Gaussian Naive Bayes can be used with Gaussian distributed data to build a classification model.
Building a Gaussian Naive Bayes Model
Building a Gaussian Naive Bayes Model
Use select functions to build a Gaussian Naive Bayes model in Python.
Understanding Support Vector Machines
Understanding Support Vector Machines
Learn how SVM bisects data into 2 in order to perform binary classification.
Configuring Support Vector Machine Parameters
Configuring Support Vector Machine Parameters
Learn how SVM models can be fine-tuned by adjusting the kernel, the Gamma parameter, and the C parameter.
Building a Support Vector Machine Model
Building a Support Vector Machine Model
Build an SVM model in Python and adjust the kernel, gamma and c arguments.
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