Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data | Natural Language Processing in Action: Understanding, analyzing, and generating text with Python | Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning | Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning | Web Scraping with Python: Collecting More Data from the Modern Web | Feature Engineering for Machine Learning Models: Principles and Techniques for Data Scientists | Natural Language Processing with Python
From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning.
You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems.
- Preprocess and vectorize text into high-dimensional feature representations
- Perform document classification and topic modeling
- Steer the model selection process with visual diagnostics
- Extract key phrases, named entities, and graph structures to reason about data in text
- Build a dialog framework to enable chatbots and language-driven interaction
- Use Spark to scale processing power and neural networks to scale model complexity
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