Unleash the power of Python libraries like Gensim, NLTK, SpaCy, and Keras to solve common and not-so-common challenges faced in the NLP domain.
* Implement full-fledged intelligent linguistic NLP projects using the Python ecosystem
* Use machine learning and deep learning techniques to perform smart language processing
* Learn how to apply various Python libraries to solve challenging issues faced by NLP practitioners across domains
Natural Language Processing is the application of computational techniques to the analysis and synthesis of natural language and speech. This book will test your expertise by bringing the best of machine learning and deep learning techniques to build full-fledged NLP projects.
Each project will grow in complexity and showcase various methodologies, optimizing tips, tricks and more. You will start with projects covering traditional NLP issues like sentiment analysis, segmentation and topic extraction. Moving on, you will learn about the clustering of data using various Python libraries like Gensim, NLTK, and scikit-learn. Then, you will learn how to build an advanced search recommendation engine and product categorization system that provides enhanced results over traditional models. You will use modern Python libraries to build projects like chatbots, automated text generation system and more. You will implement advanced deep learning concepts such as hybrid NLP techniques, transfer learning, Bi-directional LSTMs to build advanced NLP models.
By the end of this book, you will be well versed with the required expertise to solve common and also unique challenges faced in NLP domain. You will gain the required expertise to use your skills to contribute to online research work and competitions.
What you will learn
* Learn to create Question Answer Chatbot using NLTK and Transfer learning
* Solve a problem for an E-commerce company using Deep Neural Networks and Keras Library
* Hands-on experience in creating recommender systems using word embedding techniques
* Explore text, sentiment classification, and more using the machine learning and deep learning algorithms
* Use Hierarchical and K-Means clustering to group TED talks based on the description
* Learn to build advanced information extraction techniques using Gensim and various python libraries
Who This Book Is For
This book is for data scientists, machine learning engineers, and deep learning developers who are working towards building advanced intelligent natural language applications using machine learning and deep learning algorithms. We expect readers with working knowledge of Python and basics in NLP domain.
Publisher: Packt Publishing Limited
Number of pages: 313
Dimensions: 235 x 191 mm