Angielski
Natural Language Processing (NLP) has proven to be useful in a wide range of applications. Because of this, extracting information from text data sets requires attention to methods, techniques, and approaches.
'Python Text Mining' includes a number of application cases, demonstrations, and approaches that will help you deepen your understanding of feature extraction from data sets. You will get an understanding of good information retrieval, a critical step in accomplishing many machine learning tasks. We will learn to classify text into discrete segments solely on the basis of model properties, not on the basis of user-supplied criteria. The book will walk you through many methodologies, such as classification, that will enable you to rapidly construct recommendation engines, subject segmentation, and sentiment analysis applications. Toward the end, we will also look at machine translation and transfer learning.
By the end of this book, you'll know exactly how to gather web-based text, process it, and then apply it to the development of NLP applications.
1. Basic Text Processing Techniques
2. Text to Numbers
3. Word Embeddings
4. Topic Modeling
5. Unsupervised Sentiment Classification
6. Text Classification Using ML
7. Text Classification Using Deep learning
8. Recommendation engine
9. Transfer Learning
