Natural Language Processing (NLP) is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. NLP is considered as a sub-field of artificial intelligence and has significant overlap with the field of computational linguistics. It is concerned with the interactions between computers and human (natural) languages. Natural language generation systems convert information from computer databases into readable human language, and Natural language understanding systems convert human language into representations that are easier for computer programs to manipulate. NLP encompasses both text and speech, but work on speech processing has evolved into a separate field.
Welcome to your first step into the Natural Language Processing and Text Mining world! This is your risk-free approach (30-day refund policy) to delve deep into the fundamentals which Google, Amazon and Microsoft base themselves on when working with text data. Natural Language Processing is one of the most exciting fields in Data Science and Analytics nowadays. The ability to make a computer understand words and phrases is a technological innovation that brought a huge transformation to tasks such as Information Retrieval, Translation or Text Classification. In this course we are going to learn the fundamentals of working with Text data in Python and discuss the most important techniques that you should know to start your journey in Natural Language Processing.
GETTING STARTED Natural language toolkit (NLTK) is the most popular library for natural language processing (NLP) which was written in Python and has a big community behind it. NLTK also is very easy to learn, actually, it's the easiest natural language processing (NLP) library that you'll use New What you'll learn Description This course is aimed at people who are new to natural language processing (NLP). The course has two main sections, the first section explains all of the main concepts such as tokenization, parts of speech tagging, named entity extraction and so on with examples and code, the second section looks at ideas like text summarization and sentiment analysis and how we can use the core concepts from part one to solve these problems.
As the ecommerce landscape becomes more customer-friendly, brick-and-mortar merchants are turning to AI for ways of improving the in-store customer experience. As artificial intelligence (AI) begins to penetrate every niche of the digital world, developers are beginning to ask if it can improve the ecommerce experience as well. The question is timely and the answer is an emphatic "Yes!" Although ecommerce has disrupted the traditional marketplace, it has yet to achieve its full potential. If you've noticed that conversion rates on your site are low, you're not alone: Online cart abandonment rates in 2016 are hovering around an average of 70 percent.