Natural Language Processing Crash Course For Beginners

Natural Language Processing Crash Course for Beginners Artificial Intelligence (AI) isn't the latest fad! The reason is AI has been around since 1956, and its relevance is evident in every field today. Artificial Intelligence incorporates human intelligence into machines. Machine Learning (ML), a branch of AI, enables machines to learn by themselves. Deep Learning (DL), a subfield of Machine Learning, uses algorithms that are inspired by the functioning of the human brain. Natural Language Processing (NLP) combines computational linguistics and Artificial Intelligence, enabling computers and humans to communicate seamlessly. And NLP is immensely powerful and impactful as every business is looking to integrate it into their day to day dealings. How Is This Book Different? This book by AI Publishing is carefully crafted, giving equal importance to the theoretical concepts as well as the practical aspects of natural language processing. In each chapter of the second half of the book, the theoretical concepts of different types of deep learning and NLP techniques have been covered in-depth, followed by practical examples. You will learn how to apply different NLP techniques using the TensorFlow and Keras libraries for Python. Each chapter contains exercises that are designed to evaluate your understanding of the concepts covered in that chapter. Also, in the Resources section of each chapter, you can access the Python notebook. The author has also compiled a list of hands-on NLP projects and competitions that you can try on your own. The main benefit of purchasing this book is you get immediate access to all the extra learning material presented with this book--Python codes, exercises, PDFs, and references--on the publisher's website without having to spend an extra cent. You can download the datasets used in this book at runtime, or you can access them in the Resources/Datasets folder. The author holds your hand through everything. He provides you a step by step explanation of the installation of the software needed to implement the various NLP techniques in this book. You can start experimenting with the practical aspects of NLP right from the beginning. Even if you are new to Python, you'll find the ultra-short course on Python programming language in the second chapter immensely helpful. You get all the codes and datasets with this book. So, if you have access to a computer with the internet, you can get started. The topics covered include: What is Natural Language Processing? Environment Setup and Python Crash Course Introduction to Deep Learning Text Cleaning and Manipulation Common NLP Tasks Importing Text Data from Various Sources Word Embeddings: Converting Words to Numbers IMDB Movies Sentimental Analysis Ham and Spam Message Classification Text Summarization and Topic Modeling Text Classification with Deep Learning Text Translation Using Seq2Seq Model State of the Art NLP with BERT Transformers Hands-on NLP Projects/Articles for Practice Exercise Solutions Click the BUY button and download the book now to start your Natural Language Processing journey.

Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another. In this new laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about natural language processing. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects.

"Introduction to Natural Language Processing: A practical guide for beginners" is a book that provides an overview of the field of natural language processing (NLP) and its applications. It is intended for individuals with little to no experience in the area. It aims to provide a comprehensive introduction to the concepts and techniques used in NLP. The book is aimed at beginners and offers a practical guide for understanding and working with NLP techniques. It covers NLP fundamental concepts and methods, such as tokenization, stemming, lemmatization, and part-of-speech tagging. It also discusses more advanced topics such as sentiment analysis, text generation, and machine translation. The book uses Python programming language and provides examples to help readers practice and apply the concepts they learn. The book also includes real-world case studies using NLP to solve real-world problems. The book is written clearly and concisely, making it easy for beginners to understand. It provides a good foundation for those interested in pursuing a career in NLP or related fields such as machine learning, artificial intelligence, or data science. It is also helpful for professionals who want to understand NLP and its applications in their areas. Overall, "Introduction to Natural Language Processing: A practical guide for beginners" is an excellent resource for anyone interested in learning about NLP. Whether you are a student, researcher, or professional, this book provides a comprehensive introduction to the field of NLP. It will help you understand and apply the concepts and techniques of this exciting field.

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Ready to start this new journey into the Python’s world? Python is the ideal language to learn for budding developers. It is a modern object-oriented programming language with easy to read code and an extensive internet bank of modules. It offers high-level dynamic data types, many built-in functions, and operators, classes, garbage collection, and supports dynamic typing. Python runs on just about any device. Python is an OSI approved open-source software application that makes it free to download and install. Python For Beginners: A crash course to learn Python Programming in 1 Week will take you through the basics of getting started with Python programming step by step. This tutorial will teach you everything you need to know to get you to the next programming level. The book covers all the Python basics, with follow-along examples and exercises, giving you a hands-on learning approach. By the time you have made your way through the book, you will be ready to tackle the beginner’s and a few intermediate projects waiting for you at the end of it. This book covers where to and how to download and install Python. You will learn how to download and install PyCharm which is an integrated development environment where you will learn to write code. The content covers all the basics such as variables, statements, functions, keywords, data types, and more. Python For Beginners: A crash course to learn Python Programming in 1 Week has everything you need to learn to comfortably move on to more advanced programming. It is an entry-level tutorial guide that makes Python easy and fun to learn. Get your copy Now

A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.

This course lays the groundwork for further study into data science with Python for those students with little to no experienceKey Features* Crash course in Python programming to build or refresh any gaps in prerequisite knowledge* Real-world projects for hands-on practice in various data science tasks* Access to all codes and datasets free to view onlineBook DescriptionData science is here to stay. The tremendous growth in the volume, velocity, and variety of data has a substantial impact on every aspect of a business. While data continues to grow exponentially, accuracy remains a problem. This is where data scientists play a decisive role.A data scientist analyzes data, discovers new insights, paints a picture, and creates a vision. And a competent data scientist will provide a business with the competitive edge it needs and to address pressing business problems.Data Science Crash Course for Beginners with Python presents you with a hands-on approach to learn data science fast. This book presents you with the tools and packages you need to kick-start data science projects to resolve problems of a practical nature. Special emphasis is laid on the main stages of a data science pipeline--data acquisition, data preparation, exploratory data analysis, data modeling and evaluation, and interpretation of the results.The author simplifies your learning by providing detailed, guided instructions through everything. The step-by-step description of the installation of the software you need to implement the various data science techniques in this book is guaranteed to make your learning easier. So, right from the beginning, you can experiment with the practical aspects of data science. By the end of this course, you will have a solid grasp on the essential concepts of data science and its most fundamental implementations, laying the groundwork for your next steps no matter your chosen direction.The code bundle for this course is available at https://www.aispublishing.net/book-data-science-01What you will learn* Consider Natural Language Processing and decision making in data science* Install Python and libraries for data science* Review Python for data science* Study data acquisition* Practice data preparation (preprocessing)* Perform exploratory data analysis* Explore data modeling and evaluation using machine learning* Interpret data and report your findings* Successfully complete several data science projectsWho this book is forThis book is specifically designed for beginners in data science looking to build foundational tools and skills quickly, utilizing the Python programming language. No prior experience is required.

Artificial intelligence is the rage today! While you may find it difficult to understand the most recent advancements in AI, it simply boils down to two most celebrated developments: Machine Learning and Deep Learning. In 2020, Deep Learning is leagues ahead because of its supremacy when it comes to accuracy, especially when trained with enormous amounts of data. Deep Learning, essentially, is a subset of Machine Learning, but it's capable of achieving tremendous power and flexibility. And the era of big data technology presents vast opportunities for incredible innovations in deep learning. How Is This Book Different? This book gives equal importance to the theoretical as well as practical aspects of deep learning. You will understand how high-performing deep learning algorithms work. In every chapter, the theoretical explanation of the different types of deep learning techniques is followed by practical examples. You will learn how to implement different deep learning techniques using the TensorFlow Keras library for Python. Each chapter contains exercises that you can use to assess your understanding of the concepts explained in that chapter. Also, in the Resources, the Python notebook for each chapter is provided. The key advantage of buying this book is you get instant access to all the extra content presented with this book--Python codes, references, exercises, and PDFs--on the publisher's website. You don't need to spend an extra cent. The datasets used in this book are either downloaded at runtime or are available in the Resources/Datasets folder. Another advantage is a detailed explanation of the installation steps for the software that you will need to implement the various deep learning algorithms in this book is provided. That is, you get to experiment with the practical aspects of Deep Learning right from page 1. Even if you are new to Python, you will find the crash course on Python programming language in the first chapter immensely useful. Since all the codes and datasets are included with this book, you only need access to a computer with the internet to get started. The topics covered include: Python Crash Course Deep Learning Prerequisites: Linear and Logistic Regression Neural Networks from Scratch in Python Introduction to TensorFlow and Keras Convolutional Neural Networks Sequence Classification with Recurrent Neural Networks Deep Learning for Natural Language Processing Unsupervised Learning with Autoencoders Answers to All Exercises Click the BUY button and download the book now to start your Deep Learning journey.