Python Natural Language Processing Projects
Build next-generation language applications for analyzing varied texts using Python libraries
Published on 28. August 2020
Book
Paperback/Softback
313 pages
978-1-78980-049-4 (ISBN)
Description
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.
Key Features
* 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
Book Description
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.
Key Features
* 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
Book Description
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.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-1-78980-049-4 (9781789800494)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Akshay Kulkarni is an AI and machine learning evangelist. Data Scientist, an Author, and a Speaker. Akshay has a rich experience of building and scaling AI and machine learning businesses and creating significant client impact. He is currently the senior data scientist at Sapient's core data science team where he is part of strategy and transformation interventions through AI and works on various machine learning, deep learning, and artificial intelligence engagements by applying state-of-the-art techniques in this space. Previously he was part of Gartner and Accenture, where he scaled the analytics and data science business.
He is a regular speaker at major data science conferences. He is a visiting faculty at some of the top graduate institutes in India. In his spare time, he enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family Adarsha Shivananda is a Data Scientist, Author, Speaker, and AI Educator. He is currently working at Indegene's Product and Technology team where they enable Predictive analytics and AI features to all of their Healthcare software products. He is aiming to build a pool of exceptional data scientist within and outside of the organization to solve greater problems leveraging machine learning and AI. His core expertise involves Statistics, Machine Learning, Deep Learning, Natural language processing, and Recommendation Systems. Worked on various Data Science projects across multiple domains using different technologies and methodologies. Adarsha has worked extensively in the pharma, healthcare, retail, and marketing domains. Previously he was part of Tredence Analytics and IQVIA. He recently published a book Natural Language Processing Recipes with Apress.
He is also regular speaker and trainer at top colleges in the area of AI to educate and encourage students to take up Data Science as their carrier option through conducting seminars and workshops. He lives in Bangalore, loves to read motivational books, drive car and teach data science.
He is a regular speaker at major data science conferences. He is a visiting faculty at some of the top graduate institutes in India. In his spare time, he enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family Adarsha Shivananda is a Data Scientist, Author, Speaker, and AI Educator. He is currently working at Indegene's Product and Technology team where they enable Predictive analytics and AI features to all of their Healthcare software products. He is aiming to build a pool of exceptional data scientist within and outside of the organization to solve greater problems leveraging machine learning and AI. His core expertise involves Statistics, Machine Learning, Deep Learning, Natural language processing, and Recommendation Systems. Worked on various Data Science projects across multiple domains using different technologies and methodologies. Adarsha has worked extensively in the pharma, healthcare, retail, and marketing domains. Previously he was part of Tredence Analytics and IQVIA. He recently published a book Natural Language Processing Recipes with Apress.
He is also regular speaker and trainer at top colleges in the area of AI to educate and encourage students to take up Data Science as their carrier option through conducting seminars and workshops. He lives in Bangalore, loves to read motivational books, drive car and teach data science.