Summary  
This chapter covers core NLP techniques for transforming text into numerical data, including tokenization, stop word removal, stemming/lemmatization, vocabulary building, and word embeddings to prepare inputs for machine learning models.  

General domain of usage  
Sentiment analysis

**NLP** enables machines to read, understand, and generate human language. By applying various algorithms and models, NLP systems can perform tasks such as speech recognition, translation, summarization, and sentiment analysis.

Definition

<strong>Key tasks in NLP</strong>:  
<ul>
  <li><strong>Text preprocessing</strong>: involves cleaning the text data to make it suitable for analysis. Common preprocessing steps include tokenization, removing stop words, and stemming or lemmatization;</li>
  <li><strong>Text classification</strong>: assigning categories or labels to text data. Sentiment analysis is one example, where the goal is to classify text as positive, negative, or neutral;</li>
  <li><strong>Named entity recognition (NER)</strong>: identifying and classifying entities in text, such as names of people, organizations, locations, and dates;</li>
  <li><strong>Part-of-speech tagging</strong>: determining the grammatical structure of a sentence by identifying parts of speech like nouns, verbs, adjectives, etc.;</li>
  <li><strong>Sentiment analysis</strong>: the primary task of this section. Sentiment analysis involves determining the sentiment or emotion expressed in a piece of text. This is commonly used in analyzing social media posts, customer reviews, and feedback, and is typically performed using machine learning models trained on labeled data.</li>
</ul>


In summary, NLP is a key technology enabling machines to process and understand human language. By mastering the basics of NLP, such as text preprocessing, classification, and embeddings, you lay the foundation for more advanced tasks like sentiment analysis and beyond.


Which of the following is a key task in NLP?

Master Recurrent neural networks and their advanced variants like LSTMs and GRUs using PyTorch. Gain hands-on experience processing sequential data for practical applications. Apply these powerful models to tackle real-world challenges in time series forecasting and various Natural language processing tasks.

Covers the limitations of traditional neural networks for sequential data and introduces the fundamentals of Recurrent Neural Networks. Explains RNN architecture, types, and step-by-step implementation through basic examples and a coding challenge.

Explores common training challenges such as vanishing and exploding gradients. Introduces advanced RNN variants including LSTM and GRU, highlighting their internal mechanisms and use cases, with practical implementation examples for each.

Focuses on processing and forecasting time series data using RNN-based models. Includes data loading, preprocessing techniques, model training, and performance evaluation, with emphasis on comparing LSTM and GRU architectures.

Demonstrates the application of RNNs to text classification tasks. Covers core NLP concepts, text encoding methods, data preparation steps, and construction of an LSTM-based model for sentiment prediction.

NLP Basics