NLP Multilabel #01 - Introduction
Artificial Intelligence Training
NLP for Multilabel Classification: Simultaneous Labels
In this course, you will learn how to use NLP to classify texts into multiple categories simultaneously, exploring techniques such as vectorization and specialized Machine Learning algorithms. Get ready to solve complex multi-label classification problems! I look forward to seeing you in training!
In this chapter, you will learn the following:
📃 Summary:
#01 - Introduction
#02 - Importing the database
#03 - Types of Classifications
#04 - Exploring the dataset
#05 - Identifying labels
#06 - Applying categorical coding
#07 - How to separate the database from the model
#08 - Organizing the response variable
#09 - Separating into training and testing
#10 - Applying TfidfVectorizer
#11 - Explaining Binary Classification
#12 - Training OneVsRestClassifier model
#13 - Testing OneVsRestClassifier model
#14 - Evaluating with Precision
#15 - How Hamming Loss works
#16 - Evaluating with Hamming Loss
#17 - Explaining Chain Classification
#18 - Training ClassifierChain model
#19 - Evaluating model ClassifierChain
#20 - Binary Relevance with SKMultilearn
#21 - How the MLKNN model works
#22 - Training the MLKNN model
#23 - Running all the models
#24 - Conclusion
If you want to develop these valuable skills in Artificial Intelligence and start your path to an exciting and challenging career, then the Bootcamp Xperience is for you. Sign up now and discover everything that Artificial Intelligence has to offer!
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