Natural Language Processing - using Python and AI

Duration: 1 Day

1. Hands-On Learning: 80% practical experience and 20% theory to prepare you for independent NLP projects.
2. Comprehensive Coverage: Basic, Intermediate, and Advanced NLP concepts.
3. Tools and Libraries: NLTK, regex, Stanford NLP, TextBlob, and data cleaning techniques.
4. Entity Resolution: Techniques for identifying and merging different representations of the same entity.
5. Feature Extraction: Converting text into features for analysis.
6. Word Embedding: Understanding and implementing word embedding techniques.
7. Word2Vec and GloVe: Mastering these popular word embedding models.
8. Word Sense Disambiguation: Techniques to determine the meaning of words in context.
9. Speech Recognition: Basics of converting speech to text.
10. String Similarity: Methods for comparing the similarity between two strings.
11. Language Translation: Techniques for automatic translation between languages.
12. Computational Linguistics: Applying computational techniques to linguistic problems.
13. Classification Techniques: Using Random Forest, Naive Bayes, and XGBoost for text classification.
14. Deep Learning Classifications: Implementing classifications with TensorFlow (tf.keras).
15. Sentiment Analysis: Determining the sentiment of text data.
16. Clustering: K-means clustering techniques for text data.
17. Topic Modeling: Identifying topics within a corpus of text.
18. Model Evaluation: Understanding Bias vs. Variance to evaluate model performance

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• Course notes, exercises and code examples

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