Natural Language Processing Projects

Leverage machine learning and deep learning techniques to build fully-fledged natural language processing (NLP) projects. Projects throughout this book grow in complexity and showcase methodologies, optimizing tips, and tricks to solve various business problems. You will use modern Python libraries and algorithms to build end-to-end NLP projects. The book starts with an overview of natural language processing (NLP) and artificial intelligence to provide a quick refresher on algorithms. Next, it covers end-to-end NLP projects beginning with traditional algorithms and projects such as customer review sentiment and emotion detection, topic modeling, and document clustering. From there, it delves into e-commerce related projects such as product categorization using the description of the product, a search engine to retrieve the relevant content, and a content-based recommendation system to enhance user experience. Moving forward, it explains how to build systems to find similar sentences using contextual embedding, summarizing huge documents using recurrent neural networks (RNN), automatic word suggestion using long short-term memory networks (LSTM), and how to build a chatbot using transfer learning. It concludes with an exploration of next-generation AI and algorithms in the research space.

By the end of this book, you will have the knowledge needed to solve various business problems using NLP techniques.

What You Will Learn

• Implement full-fledged intelligent NLP applications with Python
• Translate real-world business problem on text data with NLP techniques
• Leverage machine learning and deep learning techniques to perform smart language processing

• Gain hands-on experience implementing end-to-end search engine information retrieval, text summarization, chatbots, text generation, document clustering and product classification,and more
  Who This Book Is For
  Data scientists, machine learning engineers, and deep learning professionals looking to build natural language applications using Python

  Covers NLP concepts and life cycle with simple and easy-to-follow end-to-end projects in Python Includes the latest industry algorithms to implement and explain concepts and applications Source code available at github.com/Apress/Natural-Language-Processing-Projects

  Autorentext
  Akshay R Kulkarni is an AI and machine learning evangelist and a thought leader. He has consulted several Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He is a Google developer, Author, and a regular speaker at major AI and data science conferences including Strata, O'Reilly AI Conf, and GIDS. He is a visiting faculty member for some of the top graduate institutes in India. In 2019, he has been also featured as one of the top 40 under 40 Data Scientists 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 Data science and MLOps Leader. He is working on creating world-class MLOps capabilities to ensure continuous value delivery from AI. He aims to build a pool of exceptional data scientists within and outside of the organization to solve problems through training programs, and always wants to stay ahead of the curve. He has worked extensively in the pharma, healthcare, CPG, retail, and marketing domains. He lives in Bangalore and loves to read and teach data science. Anoosh Kulkarni is a data scientist and an AI consultant. He has worked with global clients across multiple domains and helped them solve their business problems using machine learning (ML), natural language processing (NLP), and deep learning. Anoosh is passionate about guiding and mentoring people in their data science journey. He leads data science/machine learning meet-ups and helps aspiring data scientists navigate their careers. He also conducts ML/AI workshops at universities and is actively involved in conducting webinars, talks, and sessions on AI and data science. He lives in Bangalore with his family. V Adithya Krishnan is a data scientist and ML Ops Engineer. He has worked with various global clients across multiple domains and helped them to solve their business problems extensively using advanced Machine learning (ML) applications. He has experience across multiple fields of AI-ML, including, Time-series forecasting, Deep Learning, NLP, ML Operations, Image processing, and data analytics. Presently, he is developing a state-of-the-art value observability suite for models in production, which includes continuous model and data monitoring along with the business value realized. He also published a paper at an IEEE conference, "Deep Learning Based Approach for Range Estimation", written in collaboration with the DRDO. He lives in Chennai with his family.

  Zusammenfassung
  Intermediate-Advanced user level

  Inhalt
  Chapter 1: Natural Language Processing and Artificial Intelligence Overview.- Chapter 2: Product360 - Sentiment and Emotion Detector.- Chapter 3: TED Talks Segmentation and Topics
  Extraction Using Machine Learning.- Chapter 4: Enhancing E-commerce Using an Advanced Search
  Engine and Recommendation System.- Chapter 5: Creating a Resume Parsing, Screening, and Shortlisting
  System.- Chapter 6: Creating an E-commerce Product Categorization Model Using Deep Learning.- Chapter 7: Predicting Duplicate Questions in Quora.- Chapter 8: Named Entity Recognition Using CRF and
  BERT.- Chapter 9: Building a Chatbot Using Transfer Learning.- Chapter 10: News Headline Summarization.- Chapter 11: Text Generation - Next Word Prediction.- Chapter 12: Conclusion and Future Trends.