Unleashing Machine Learning Potentials in Natural Language Processing
In this chapter, we will delve into the fundamentals of Machine Learning (ML) and preprocessing techniques that are essential for natural language processing (NLP) tasks. ML is a powerful tool for building models that can learn from data, and NLP is one of the most exciting and challenging applications of ML.
By the end of this chapter, you will have gained a comprehensive understanding of data exploration, preprocessing, and data split, know how to deal with imbalanced data techniques, and learned about some of the common ML models required for successful ML, particularly in the context of NLP.
The following topics will be covered in this chapter:
- Data exploration
- Common ML models
- Model underfitting and overfitting
- Splitting data
- Hyperparameter tuning
- Ensemble models
- Handling imbalanced data
- Dealing with correlated data
