Natural Language Processing Course

This course covers Natural Language Processing principles.

What You'll Learn: ( Hands-On Learning )

Comprehensive Coverage: Basic, Intermediate, and Advanced NLP concepts.

Tools and Libraries: NLTK, regex, Stanford NLP, TextBlob, and data cleaning techniques.

Entity Resolution: Techniques for identifying and merging different representations of the same entity.

Feature Extraction: Converting text into features for analysis.

Word Embedding: Understanding and implementing word embedding techniques.

Word2Vec and GloVe: Mastering these popular word embedding models.

Word Sense Disambiguation: Techniques to determine the meaning of words in context.

Speech Recognition: Basics of converting speech to text.

String Similarity: Methods for comparing the similarity between two strings.

Language Translation: Techniques for automatic translation between languages.

Computational Linguistics: Applying computational techniques to linguistic problems.

Classification Techniques: Using Random Forest, Naive Bayes, and XGBoost for text classification.

Deep Learning Classifications: Implementing classifications with TensorFlow (tf.keras).

Sentiment Analysis: Determining the sentiment of text data.

Clustering: K-means clustering techniques for text data.

Topic Modeling: Identifying topics within a corpus of text.

Model Evaluation: Understanding Bias vs. Variance to evaluate model performance.