Using natural language processing (NLP) tools, an organization can analyze their review data and predict the sentiments of their customers. In this course, we'll learn how to implement NLP tools to solve a business problem end-to-end. To begin, learn about loading, exploring, and preprocessing business data. Next, explore various linguistic features and feature engineering methods for data and practice building machine learning (ML) models for sentiment prediction. Finally, examine the automation options available for building and deploying models. After completing this course, you will be able to solve NLP problems for enterprises end-to-end by leveraging a variety of concepts and tools.

Machine learning (ML) is one of the most important toolsets available in the enterprise world. It gives predictive powers to data that can be leveraged to investigate future behaviors and patterns. It can help companies proactively improve their business and help optimize their revenue. Learn how to leverage machine learning to make predictions with language data. Explore the ML pipelines and common models used for Natural Language Processing (NLP). Examine a real-world use case of identifying sarcasm in text and discover the machine learning techniques suitable for NLP problems. Learn different vectorization and feature engineering methods for text data, exploratory data analysis for text, model building, and evaluation for predicting from text data and how to tune those models to achieve better results. After completing this course, you'll be able to illustrate the use of machine learning to solve NLP problems and demonstrate the use of NLP feature engineering.

There are many tools available in the Natural Language Processing (NLP) tool landscape. With single tools, you can do a lot of things faster. However, using multiple state-of-art tools together, you can solve many problems and extract multiple patterns from your data. In this course, you will discover many important tools available for NLP such as polyglot, Genism, TextBlob, and CoreNLP. Explore their benefits and how they stand against each other for performing any NLP task. Learn to implement core linguistic features like POS tags, NER, and morphological analysis using the tools discussed earlier in the course. Discover defining features of each tool such as multiple language support, language detection, topic models, sentiment extractions, part of speech (POS) driven patterns, and transliterations. Upon completion of this course, you will feel confident with the Python tool ecosystem for NLP and will be able to perform state-of-art pattern extraction on any kind of text data.

Sometimes, business wants to find similar-sounding words, specific word occurrences, and sentiment from the raw text. Having learned to extract foundational linguistic features from the text, the next objective is to learn the heuristic approach to extract non-foundational features which are subjective. In this course, learn how to extract synonyms and hypernyms with WordNet, a widely used tool from the Natural Language Toolkit (NLTK). Next, explore the regex module in Python to perform NLTK chunking and to extract specific required patterns. Finally, you will solve a real-world use case by finding sentiments of movies using WordNet. After comleting this course, you will be able to use a heuristic approach of natural language processing (NLP) and to illustrate the use of WordNet, NLTK chunking, regex, and SentiWordNet.