Explore the field of Natural Language Processing using Spacy, NLTK, PyTorch, Text Preprocessing, Embeddings, Word2Vec, and Deep Learning
Key Features
- Apply the concepts to any language to build customized NLP models
- Learn machine learning concepts in a more practical way
- Build your own applications for automatic text generation and language translators
Book Description
Natural Language Processing (NLP), a subdivision of Artificial Intelligence (AI), is the ability of a computer to understand human language the way it's spoken and written. Human language is typically referred to as natural language.
Humans also have different sensors. For instance, ears perform the function of hearing and eyes perform the function of seeing. Similarly, computers have programs for reading and microphones for collecting audio. Just as the human brain processes an input, a computer program processes a specific input. And during processing, the program converts the input to code that the computer understands.
This course, Natural Language Processing (NLP), Theory and Practice in Python, introduces you to the concepts, tools, and techniques of machine learning for text data. You will learn the elementary concepts as well as emerging trends in the field of NLP. You will also learn about the implementation and evaluation of different NLP applications using deep learning methods.
Code bundles are available here: https://github.com/PacktPublishing/NLP-Natural-Language-Processing-in-Python-for-Beginners
What you will learn
- Learn the fundamentals of NLP using datasets
- Explore language models and their uses in speech recognition
- Learn to use software tools such as SpaCY, NLTK, Gensim, and PyTorch
- Learn the concepts of deep learning theory
- Explore linear subspaces for word embeddings
- Understand the architecture of neural networks
Who this book is for
This course is for complete beginners who are new to NLP, people who want to upgrade their Python programming skills for NLP, and individuals who are passionate about numbers and programming such as data scientists, data analysts, and machine learning practitioners.
