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PyCaret is an low-code, open-source, machine learning library for Python. It can be used in machine learning tasks such as data preparation and model deployment.

In this course, you will learn multiple topics related to machine learning. You will start with a brief introduction to the basic concepts of machine learning, and then continue with case studies of regression, classification, clustering, and anomaly detection based on the respective modules of the PyCaret library. Finally, we will focus on using the Streamlit library to develop and deploy machine learning applications.

By the end of this course, you will have the skills to deploy the robust PyCaret library for any of your machine learning projects.

Simplifying Machine Learning with PyCaret in Python

In this chapter, which NLP model is used for topic modeling?

In this chapter, which NLP model is used for topic modeling?

What is the word cloud used for in NLP?

Stopword removal

Numeric and special character removal

Word tokenization

Bigram and trigram extraction

Lemmatization

Numeric and special character removal

Word tokenization

Stopword removal

Lemmatization

Bigram and trigram extraction


Word tokenization

Numeric and special character removal

Stopword removal

Bigram and trigram extraction

Lemmatization

Numeric and special character removal

Word tokenization

Stopword removal

Bigram and trigram extraction

Lemmatization

During the initialization of an NLP environment, different steps are performed in a specific sequence. Which option below is the correct sequence of these steps?

During the initialization of an NLP environment, different steps are performed in a specific sequence. Which option below is the correct sequence of these steps?

When the dataset is imbalanced, which parameter do we set to `True`?

When the dataset is imbalanced, which parameter do we set to <code>True</code>?

In which format do we save machine learning model files?

In which format do we save machine learning model files?

This quiz will test your knowledge about natural language processing.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Quiz: Natural Language Processing