**Data Science and Machine Learning Series: Naive Bayes Classifier Advanced Concepts**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 04m | 287 MB

Our last video in this series introduced the Naive Bayes Classifier and now this video will cover more advanced concepts using this powerful algorithm. Follow along with machine learning expert Advait Jayant through a combination of lecture and hands-on to practice applying advanced Naive Bayes applications in Python using the pandas and numpy libraries.

The following seven topics will be covered in this Data Science and Machine Learning course:

- The Multivariate Bernoulli Naive Bayes Classifier. Apply the Multivariate Bernoulli Naive Bayes Classifier to perform text classification in this first topic in the Data Science and Machine Learning Series. Follow along with Advait and use the Bag of Words algorithm.
- The Multinomial Event Naive Bayes Model. Apply the Multinomial Event Naive Bayes Model in this second topic in the Data Science and Machine Learning Series. Follow along with Advait and see how the Multinomial Event Naive Bayes Model differs from the Multivariate Bernoulli Naive Bayes Classifier.
- The Gaussian Naive Bayes Model. Apply probability distribution using the Gaussian Naive Bayes model in this third topic in the Data Science and Machine Learning Series. Follow along with Advait and apply this powerful algorithm in Python using the Scikit-learn library.
- MNIST (Modified National Institute of Standards and Technology dataset) Classification using Multinomial Event and Gaussian Models. Compare the Multinomial Event Naive Bayes and Gaussian Naive Bayes models in how they perform MNIST (Modified National Institute of Standards and Technology dataset) classification in this fourth topic in the Data Science and Machine Learning Series. Follow along with Advait and contrast these powerful algorithms in Python using the Scikit-learn library.
- Movie Review Classification using the Naive Bayes Classifier. Classify movies using the Naive Bayes Classifier in this fifth topic in the Data Science and Machine Learning Series. Follow along with Advait and practice building NLP pipelines, applying tokenization, and removing stop words in Python.
- Movie Review Prediction using the Multinomial Event and Multivariate Bernoulli Naive Bayes Models. Predict movie reviews using the Multinomial Event and Multivariate Bernoulli Naive Bayes models in this sixth topic in the Data Science and Machine Learning Series. Follow along with Advait and apply both classifiers to predict movie success using Python.
- Confusion Matrix. Apply the Confusion Matrix to better understand precision and recall in this seventh topic in the Data Science and Machine Learning Series. Follow along with Advait and practice building confusion matrices using Python with the seaborn, pandas, and Matplotlib libraries.

**Table of Contents**

1 The Multivariate Bernoulli Naive Bayes Classifier

2 The Multinomial Event Naive Bayes Model

3 The Gaussian Naive Bayes Model

4 MNIST (Modified National Institute of Standards and Technology dataset) Classification using

5 Movie Review Classification using the Naive Bayes Classifier

6 Movie Review Prediction using the Multinomial Event and Multivariate Bernoulli Naive Bayes M

7 Confusion Matrix

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