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Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation
To sustain and stay at the top of the market and give absolute
comfort to the consumers, industries are using different strategies
and technologies. Natural language processing (NLP) is a technology
widely penetrating the market, irrespective of the industry and
domains. It is extensively applied in businesses today, and it is
the buzzword in every engineer's life. NLP can be implemented in
all those areas where artificial intelligence is applicable either
by simplifying the communication process or by refining and
analyzing information. Neural machine translation has improved the
imitation of professional translations over the years. When applied
in neural machine translation, NLP helps educate neural machine
networks. This can be used by industries to translate low-impact
content including emails, regulatory texts, etc. Such machine
translation tools speed up communication with partners while
enriching other business interactions. Deep Natural Language
Processing and AI Applications for Industry 5.0 provides innovative
research on the latest findings, ideas, and applications in fields
of interest that fall under the scope of NLP including
computational linguistics, deep NLP, web analysis, sentiments
analysis for business, and industry perspective. This book covers a
wide range of topics such as deep learning, deepfakes, text mining,
blockchain technology, and more, making it a crucial text for
anyone interested in NLP and artificial intelligence, including
academicians, researchers, professionals, industry experts,
business analysts, data scientists, data analysts, healthcare
system designers, intelligent system designers, practitioners, and
students.
One-stop solution for NLP practitioners, ML developers, and data
scientists to build effective NLP systems that can perform
real-world complicated tasks Key Features Apply deep learning
algorithms and techniques such as BiLSTMS, CRFs, BPE and more using
TensorFlow 2 Explore applications like text generation,
summarization, weakly supervised labelling and more Read cutting
edge material with seminal papers provided in the GitHub repository
with full working code Book DescriptionRecently, there have been
tremendous advances in NLP, and we are now moving from research
labs into practical applications. This book comes with a perfect
blend of both the theoretical and practical aspects of trending and
complex NLP techniques. The book is focused on innovative
applications in the field of NLP, language generation, and dialogue
systems. It helps you apply the concepts of pre-processing text
using techniques such as tokenization, parts of speech tagging, and
lemmatization using popular libraries such as Stanford NLP and
SpaCy. You will build Named Entity Recognition (NER) from scratch
using Conditional Random Fields and Viterbi Decoding on top of
RNNs. The book covers key emerging areas such as generating text
for use in sentence completion and text summarization, bridging
images and text by generating captions for images, and managing
dialogue aspects of chatbots. You will learn how to apply transfer
learning and fine-tuning using TensorFlow 2. Further, it covers
practical techniques that can simplify the labelling of textual
data. The book also has a working code that is adaptable to your
use cases for each tech piece. By the end of the book, you will
have an advanced knowledge of the tools, techniques and deep
learning architecture used to solve complex NLP problems. What you
will learn Grasp important pre-steps in building NLP applications
like POS tagging Use transfer and weakly supervised learning using
libraries like Snorkel Do sentiment analysis using BERT Apply
encoder-decoder NN architectures and beam search for summarizing
texts Use Transformer models with attention to bring images and
text together Build apps that generate captions and answer
questions about images using custom Transformers Use advanced
TensorFlow techniques like learning rate annealing, custom layers,
and custom loss functions to build the latest DeepNLP models Who
this book is forThis is not an introductory book and assumes the
reader is familiar with basics of NLP and has fundamental Python
skills, as well as basic knowledge of machine learning and
undergraduate-level calculus and linear algebra. The readers who
can benefit the most from this book include intermediate ML
developers who are familiar with the basics of supervised learning
and deep learning techniques and professionals who already use
TensorFlow/Python for purposes such as data science, ML, research,
analysis, etc.
Become a proficient NLP data scientist by developing deep learning
models for NLP and extract valuable insights from structured and
unstructured data Key Features Get to grips with word embeddings,
semantics, labeling, and high-level word representations using
practical examples Learn modern approaches to NLP and explore
state-of-the-art NLP models using PyTorch Improve your NLP
applications with innovative neural networks such as RNNs, LSTMs,
and CNNs Book DescriptionIn the internet age, where an increasing
volume of text data is generated daily from social media and other
platforms, being able to make sense of that data is a crucial
skill. With this book, you'll learn how to extract valuable
insights from text by building deep learning models for natural
language processing (NLP) tasks. Starting by understanding how to
install PyTorch and using CUDA to accelerate the processing speed,
you'll explore how the NLP architecture works with the help of
practical examples. This PyTorch NLP book will guide you through
core concepts such as word embeddings, CBOW, and tokenization in
PyTorch. You'll then learn techniques for processing textual data
and see how deep learning can be used for NLP tasks. The book
demonstrates how to implement deep learning and neural network
architectures to build models that will allow you to classify and
translate text and perform sentiment analysis. Finally, you'll
learn how to build advanced NLP models, such as conversational
chatbots. By the end of this book, you'll not only have understood
the different NLP problems that can be solved using deep learning
with PyTorch, but also be able to build models to solve them. What
you will learn Use NLP techniques for understanding, processing,
and generating text Understand PyTorch, its applications and how it
can be used to build deep linguistic models Explore the wide
variety of deep learning architectures for NLP Develop the skills
you need to process and represent both structured and unstructured
NLP data Become well-versed with state-of-the-art technologies and
exciting new developments in the NLP domain Create chatbots using
attention-based neural networks Who this book is forThis PyTorch
book is for NLP developers, machine learning and deep learning
developers, and anyone interested in building intelligent language
applications using both traditional NLP approaches and deep
learning architectures. If you're looking to adopt modern NLP
techniques and models for your development projects, this book is
for you. Working knowledge of Python programming, along with basic
working knowledge of NLP tasks, is required.
Noch niemals ist die Zusammenarbeit der deutschsprachigen mit der
angelsachsischen Welt in technischen Fragen so eindringlich und
umfassend gewesen wie in den Jahren seit dem Ende des zweiten
Weltkrieges. Das unter dem Namen des MARSHALL-Plans bekannte
Aufbauwerk des amerikanischen Volkes hat in seiner Durchfuhrung
einen besonders verstarkten Schriftverkehr uber technische
Einzelheiten mit der Notwendigkeit der deutsch-englischen
UEbersetzung mit sich gebracht; das Berg-und Huttenwesen steht
dabei mit in der vordersten Reihe der Gebiete, fur die eine solche
Aufgabe erwachsen ist. Auch ist es heute fur den Ingenieur in den
Planungsstellen, in den Betrieben, in den Statten der
wissenschaftlichen Forschung oder im Patentwesen, aber ebenso
bereits fur den Studenten der technischen Facher mehr denn je zur
zwingenden Forderung geworden, das englisch geschriebene
Fachschrifttum verfolgen zu koennen. Technisches Englisch ist nun
bekanntlich eine Sprache, die in vielen Belangen uber einen anderen
Wortschatz und eine andere Zuordnung von Begriff und Wort verfugt
als das Englisch des sonstigen taglichen Lebens oder des
schoengeistigen Schrifttums. Bedenkliche Missverstand- nisse
koennen entstehen, wenn dieser Tatsache nicht Rechnung getragen
wird. UEberdies ist die technische Sprache schnellehig wandelbar
wie die Technik selbst; die Schwierigkeiten, die daraus entstehen,
treten dem Benutzer der bisher erschienenen technischen
Fachwoerterbucher immer wieder einmal entgegen.
Implement supervised and unsupervised machine learning algorithms
using C++ libraries such as PyTorch C++ API, Caffe2, Shogun,
Shark-ML, mlpack, and dlib with the help of real-world examples and
datasets Key Features Become familiar with data processing,
performance measuring, and model selection using various C++
libraries Implement practical machine learning and deep learning
techniques to build smart models Deploy machine learning models to
work on mobile and embedded devices Book DescriptionC++ can make
your machine learning models run faster and more efficiently. This
handy guide will help you learn the fundamentals of machine
learning (ML), showing you how to use C++ libraries to get the most
out of your data. This book makes machine learning with C++ for
beginners easy with its example-based approach, demonstrating how
to implement supervised and unsupervised ML algorithms through
real-world examples. This book will get you hands-on with tuning
and optimizing a model for different use cases, assisting you with
model selection and the measurement of performance. You'll cover
techniques such as product recommendations, ensemble learning, and
anomaly detection using modern C++ libraries such as PyTorch C++
API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you'll
explore neural networks and deep learning using examples such as
image classification and sentiment analysis, which will help you
solve various problems. Later, you'll learn how to handle
production and deployment challenges on mobile and cloud platforms,
before discovering how to export and import models using the ONNX
format. By the end of this C++ book, you will have real-world
machine learning and C++ knowledge, as well as the skills to use
C++ to build powerful ML systems. What you will learn Explore how
to load and preprocess various data types to suitable C++ data
structures Employ key machine learning algorithms with various C++
libraries Understand the grid-search approach to find the best
parameters for a machine learning model Implement an algorithm for
filtering anomalies in user data using Gaussian distribution
Improve collaborative filtering to deal with dynamic user
preferences Use C++ libraries and APIs to manage model structures
and parameters Implement a C++ program to solve image
classification tasks with LeNet architecture Who this book is
forYou will find this C++ machine learning book useful if you want
to get started with machine learning algorithms and techniques
using the popular C++ language. As well as being a useful first
course in machine learning with C++, this book will also appeal to
data analysts, data scientists, and machine learning developers who
are looking to implement different machine learning models in
production using varied datasets and examples. Working knowledge of
the C++ programming language is mandatory to get started with this
book.
The concept of natural language processing has become one of the
preferred methods to better understand consumers, especially in
recent years when digital technologies and research methods have
developed exponentially. It has become apparent that when
responding to international consumers through multiple platforms
and speaking in the same language in which the consumers express
themselves, companies are improving their standings within the
public sphere. Natural Language Processing for Global and Local
Business provides research exploring the theoretical and practical
phenomenon of natural language processing through different
languages and platforms in terms of today's conditions. Featuring
coverage on a broad range of topics such as computational
linguistics, information engineering, and translation technology,
this book is ideally designed for IT specialists, academics,
researchers, students, and business professionals seeking current
research on improving and understanding the consumer experience.
Use Java and Deeplearning4j to build robust, scalable, and highly
accurate AI models from scratch Key Features Install and configure
Deeplearning4j to implement deep learning models from scratch
Explore recipes for developing, training, and fine-tuning your
neural network models in Java Model neural networks using datasets
containing images, text, and time-series data Book DescriptionJava
is one of the most widely used programming languages in the world.
With this book, you will see how to perform deep learning using
Deeplearning4j (DL4J) - the most popular Java library for training
neural networks efficiently. This book starts by showing you how to
install and configure Java and DL4J on your system. You will then
gain insights into deep learning basics and use your knowledge to
create a deep neural network for binary classification from
scratch. As you progress, you will discover how to build a
convolutional neural network (CNN) in DL4J, and understand how to
construct numeric vectors from text. This deep learning book will
also guide you through performing anomaly detection on unsupervised
data and help you set up neural networks in distributed systems
effectively. In addition to this, you will learn how to import
models from Keras and change the configuration in a pre-trained
DL4J model. Finally, you will explore benchmarking in DL4J and
optimize neural networks for optimal results. By the end of this
book, you will have a clear understanding of how you can use DL4J
to build robust deep learning applications in Java. What you will
learn Perform data normalization and wrangling using DL4J Build
deep neural networks using DL4J Implement CNNs to solve image
classification problems Train autoencoders to solve anomaly
detection problems using DL4J Perform benchmarking and optimization
to improve your model's performance Implement reinforcement
learning for real-world use cases using RL4J Leverage the
capabilities of DL4J in distributed systems Who this book is forIf
you are a data scientist, machine learning developer, or a deep
learning enthusiast who wants to implement deep learning models in
Java, this book is for you. Basic understanding of Java programming
as well as some experience with machine learning and neural
networks is required to get the most out of this book.
Discover how to integrate KNIME Analytics Platform with deep
learning libraries to implement artificial intelligence solutions
Key Features Become well-versed with KNIME Analytics Platform to
perform codeless deep learning Design and build deep learning
workflows quickly and more easily using the KNIME GUI Discover
different deployment options without using a single line of code
with KNIME Analytics Platform Book DescriptionKNIME Analytics
Platform is an open source software used to create and design data
science workflows. This book is a comprehensive guide to the KNIME
GUI and KNIME deep learning integration, helping you build neural
network models without writing any code. It'll guide you in
building simple and complex neural networks through practical and
creative solutions for solving real-world data problems. Starting
with an introduction to KNIME Analytics Platform, you'll get an
overview of simple feed-forward networks for solving simple
classification problems on relatively small datasets. You'll then
move on to build, train, test, and deploy more complex networks,
such as autoencoders, recurrent neural networks (RNNs), long
short-term memory (LSTM), and convolutional neural networks (CNNs).
In each chapter, depending on the network and use case, you'll
learn how to prepare data, encode incoming data, and apply best
practices. By the end of this book, you'll have learned how to
design a variety of different neural architectures and will be able
to train, test, and deploy the final network. What you will learn
Use various common nodes to transform your data into the right
structure suitable for training a neural network Understand neural
network techniques such as loss functions, backpropagation, and
hyperparameters Prepare and encode data appropriately to feed it
into the network Build and train a classic feedforward network
Develop and optimize an autoencoder network for outlier detection
Implement deep learning networks such as CNNs, RNNs, and LSTM with
the help of practical examples Deploy a trained deep learning
network on real-world data Who this book is forThis book is for
data analysts, data scientists, and deep learning developers who
are not well-versed in Python but want to learn how to use KNIME
GUI to build, train, test, and deploy neural networks with
different architectures. The practical implementations shown in the
book do not require coding or any knowledge of dedicated scripts,
so you can easily implement your knowledge into practical
applications. No prior experience of using KNIME is required to get
started with this book.
Apply modern deep learning techniques to build and train deep
neural networks using Gorgonia Key Features Gain a practical
understanding of deep learning using Golang Build complex neural
network models using Go libraries and Gorgonia Take your deep
learning model from design to deployment with this handy guide Book
DescriptionGo is an open source programming language designed by
Google for handling large-scale projects efficiently. The Go
ecosystem comprises some really powerful deep learning tools such
as DQN and CUDA. With this book, you'll be able to use these tools
to train and deploy scalable deep learning models from scratch.
This deep learning book begins by introducing you to a variety of
tools and libraries available in Go. It then takes you through
building neural networks, including activation functions and the
learning algorithms that make neural networks tick. In addition to
this, you'll learn how to build advanced architectures such as
autoencoders, restricted Boltzmann machines (RBMs), convolutional
neural networks (CNNs), recurrent neural networks (RNNs), and more.
You'll also understand how you can scale model deployments on the
AWS cloud infrastructure for training and inference. By the end of
this book, you'll have mastered the art of building, training, and
deploying deep learning models in Go to solve real-world problems.
What you will learn Explore the Go ecosystem of libraries and
communities for deep learning Get to grips with Neural Networks,
their history, and how they work Design and implement Deep Neural
Networks in Go Get a strong foundation of concepts such as
Backpropagation and Momentum Build Variational Autoencoders and
Restricted Boltzmann Machines using Go Build models with CUDA and
benchmark CPU and GPU models Who this book is forThis book is for
data scientists, machine learning engineers, and AI developers who
want to build state-of-the-art deep learning models using Go.
Familiarity with basic machine learning concepts and Go programming
is required to get the best out of this book.
Make NLP easy by building chatbots and models, and executing
various NLP tasks to gain data-driven insights from raw text data
Key Features Get familiar with key natural language processing
(NLP) concepts and terminology Explore the functionalities and
features of popular NLP tools Learn how to use Python programming
and third-party libraries to perform NLP tasks Book DescriptionDo
you want to learn how to communicate with computer systems using
Natural Language Processing (NLP) techniques, or make a machine
understand human sentiments? Do you want to build applications like
Siri, Alexa, or chatbots, even if you've never done it before? With
The Natural Language Processing Workshop, you can expect to make
consistent progress as a beginner, and get up to speed in an
interactive way, with the help of hands-on activities and fun
exercises. The book starts with an introduction to NLP. You'll
study different approaches to NLP tasks, and perform exercises in
Python to understand the process of preparing datasets for NLP
models. Next, you'll use advanced NLP algorithms and visualization
techniques to collect datasets from open websites, and to summarize
and generate random text from a document. In the final chapters,
you'll use NLP to create a chatbot that detects positive or
negative sentiment in text documents such as movie reviews. By the
end of this book, you'll be equipped with the essential NLP tools
and techniques you need to solve common business problems that
involve processing text. What you will learn Obtain, verify, clean
and transform text data into a correct format for use Use methods
such as tokenization and stemming for text extraction Develop a
classifier to classify comments in Wikipedia articles Collect data
from open websites with the help of web scraping Train a model to
detect topics in a set of documents using topic modeling Discover
techniques to represent text as word and document vectors Who this
book is forThis book is for beginner to mid-level data scientists,
machine learning developers, and NLP enthusiasts. A basic
understanding of machine learning and NLP is required to help you
grasp the topics in this workshop more quickly.
Gain the knowledge of various deep neural network architectures and
their application areas to conquer your NLP issues. Key Features
Gain insights into the basic building blocks of natural language
processing Learn how to select the best deep neural network to
solve your NLP problems Explore convolutional and recurrent neural
networks and long short-term memory networks Book
DescriptionApplying deep learning approaches to various NLP tasks
can take your computational algorithms to a completely new level in
terms of speed and accuracy. Deep Learning for Natural Language
Processing starts off by highlighting the basic building blocks of
the natural language processing domain. The book goes on to
introduce the problems that you can solve using state-of-the-art
neural network models. After this, delving into the various neural
network architectures and their specific areas of application will
help you to understand how to select the best model to suit your
needs. As you advance through this deep learning book, you'll study
convolutional, recurrent, and recursive neural networks, in
addition to covering long short-term memory networks (LSTM).
Understanding these networks will help you to implement their
models using Keras. In the later chapters, you will be able to
develop a trigger word detection application using NLP techniques
such as attention model and beam search. By the end of this book,
you will not only have sound knowledge of natural language
processing but also be able to select the best text pre-processing
and neural network models to solve a number of NLP issues. What you
will learn Understand various pre-processing techniques for deep
learning problems Build a vector representation of text using
word2vec and GloVe Create a named entity recognizer and
parts-of-speech tagger with Apache OpenNLP Build a machine
translation model in Keras Develop a text generation application
using LSTM Build a trigger word detection application using an
attention model Who this book is forIf you're an aspiring data
scientist looking for an introduction to deep learning in the NLP
domain, this is just the book for you. Strong working knowledge of
Python, linear algebra, and machine learning is a must.
Design and create neural networks with deep learning and artificial
intelligence principles using OpenAI Gym, TensorFlow, and Keras Key
Features Explore neural network architecture and understand how it
functions Learn algorithms to solve common problems using back
propagation and perceptrons Understand how to apply neural networks
to applications with the help of useful illustrations Book
DescriptionNeural networks play a very important role in deep
learning and artificial intelligence (AI), with applications in a
wide variety of domains, right from medical diagnosis, to financial
forecasting, and even machine diagnostics. Hands-On Neural Networks
is designed to guide you through learning about neural networks in
a practical way. The book will get you started by giving you a
brief introduction to perceptron networks. You will then gain
insights into machine learning and also understand what the future
of AI could look like. Next, you will study how embeddings can be
used to process textual data and the role of long short-term memory
networks (LSTMs) in helping you solve common natural language
processing (NLP) problems. The later chapters will demonstrate how
you can implement advanced concepts including transfer learning,
generative adversarial networks (GANs), autoencoders, and
reinforcement learning. Finally, you can look forward to further
content on the latest advancements in the field of neural networks.
By the end of this book, you will have the skills you need to
build, train, and optimize your own neural network model that can
be used to provide predictable solutions. What you will learn Learn
how to train a network by using backpropagation Discover how to
load and transform images for use in neural networks Study how
neural networks can be applied to a varied set of applications
Solve common challenges faced in neural network development
Understand the transfer learning concept to solve tasks using Keras
and Visual Geometry Group (VGG) network Get up to speed with
advanced and complex deep learning concepts like LSTMs and NLP
Explore innovative algorithms like GANs and deep reinforcement
learning Who this book is forIf you are interested in artificial
intelligence and deep learning and want to further your skills,
then this intermediate-level book is for you. Some knowledge of
statistics will help you get the most out of this book.
Get well-versed with traditional as well as modern natural language
processing concepts and techniques Key Features Perform various NLP
tasks to build linguistic applications using Python libraries
Understand, analyze, and generate text to provide accurate results
Interpret human language using various NLP concepts, methodologies,
and tools Book DescriptionNatural Language Processing (NLP) is the
subfield in computational linguistics that enables computers to
understand, process, and analyze text. This book caters to the
unmet demand for hands-on training of NLP concepts and provides
exposure to real-world applications along with a solid theoretical
grounding. This book starts by introducing you to the field of NLP
and its applications, along with the modern Python libraries that
you'll use to build your NLP-powered apps. With the help of
practical examples, you'll learn how to build reasonably
sophisticated NLP applications, and cover various methodologies and
challenges in deploying NLP applications in the real world. You'll
cover key NLP tasks such as text classification, semantic
embedding, sentiment analysis, machine translation, and developing
a chatbot using machine learning and deep learning techniques. The
book will also help you discover how machine learning techniques
play a vital role in making your linguistic apps smart. Every
chapter is accompanied by examples of real-world applications to
help you build impressive NLP applications of your own. By the end
of this NLP book, you'll be able to work with language data, use
machine learning to identify patterns in text, and get acquainted
with the advancements in NLP. What you will learn Understand how
NLP powers modern applications Explore key NLP techniques to build
your natural language vocabulary Transform text data into
mathematical data structures and learn how to improve text mining
models Discover how various neural network architectures work with
natural language data Get the hang of building sophisticated text
processing models using machine learning and deep learning Check
out state-of-the-art architectures that have revolutionized
research in the NLP domain Who this book is forThis NLP Python book
is for anyone looking to learn NLP's theoretical and practical
aspects alike. It starts with the basics and gradually covers
advanced concepts to make it easy to follow for readers with
varying levels of NLP proficiency. This comprehensive guide will
help you develop a thorough understanding of the NLP methodologies
for building linguistic applications; however, working knowledge of
Python programming language and high school level mathematics is
expected.
Updated and revised second edition of the bestselling guide to
advanced deep learning with TensorFlow 2 and Keras Key Features
Explore the most advanced deep learning techniques that drive
modern AI results New coverage of unsupervised deep learning using
mutual information, object detection, and semantic segmentation
Completely updated for TensorFlow 2.x Book DescriptionAdvanced Deep
Learning with TensorFlow 2 and Keras, Second Edition is a
completely updated edition of the bestselling guide to the advanced
deep learning techniques available today. Revised for TensorFlow
2.x, this edition introduces you to the practical side of deep
learning with new chapters on unsupervised learning using mutual
information, object detection (SSD), and semantic segmentation (FCN
and PSPNet), further allowing you to create your own cutting-edge
AI projects. Using Keras as an open-source deep learning library,
the book features hands-on projects that show you how to create
more effective AI with the most up-to-date techniques. Starting
with an overview of multi-layer perceptrons (MLPs), convolutional
neural networks (CNNs), and recurrent neural networks (RNNs), the
book then introduces more cutting-edge techniques as you explore
deep neural network architectures, including ResNet and DenseNet,
and how to create autoencoders. You will then learn about GANs, and
how they can unlock new levels of AI performance. Next, you'll
discover how a variational autoencoder (VAE) is implemented, and
how GANs and VAEs have the generative power to synthesize data that
can be extremely convincing to humans. You'll also learn to
implement DRL such as Deep Q-Learning and Policy Gradient Methods,
which are critical to many modern results in AI. What you will
learn Use mutual information maximization techniques to perform
unsupervised learning Use segmentation to identify the pixel-wise
class of each object in an image Identify both the bounding box and
class of objects in an image using object detection Learn the
building blocks for advanced techniques - MLPss, CNN, and RNNs
Understand deep neural networks - including ResNet and DenseNet
Understand and build autoregressive models - autoencoders, VAEs,
and GANs Discover and implement deep reinforcement learning methods
Who this book is forThis is not an introductory book, so fluency
with Python is required. The reader should also be familiar with
some machine learning approaches, and practical experience with DL
will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not
required but is recommended.
Use Python and NLTK (Natural Language Toolkit) to build out your
own text classifiers and solve common NLP problems. Key Features
Assimilate key NLP concepts and terminologies Explore popular NLP
tools and techniques Gain practical experience using NLP in
application code Book DescriptionIf NLP hasn't been your forte,
Natural Language Processing Fundamentals will make sure you set off
to a steady start. This comprehensive guide will show you how to
effectively use Python libraries and NLP concepts to solve various
problems. You'll be introduced to natural language processing and
its applications through examples and exercises. This will be
followed by an introduction to the initial stages of solving a
problem, which includes problem definition, getting text data, and
preparing it for modeling. With exposure to concepts like advanced
natural language processing algorithms and visualization
techniques, you'll learn how to create applications that can
extract information from unstructured data and present it as
impactful visuals. Although you will continue to learn NLP-based
techniques, the focus will gradually shift to developing useful
applications. In these sections, you'll understand how to apply NLP
techniques to answer questions as can be used in chatbots. By the
end of this book, you'll be able to accomplish a varied range of
assignments ranging from identifying the most suitable type of NLP
task for solving a problem to using a tool like spacy or gensim for
performing sentiment analysis. The book will easily equip you with
the knowledge you need to build applications that interpret human
language. What you will learn Obtain, verify, and clean data before
transforming it into a correct format for use Perform data analysis
and machine learning tasks using Python Understand the basics of
computational linguistics Build models for general natural language
processing tasks Evaluate the performance of a model with the right
metrics Visualize, quantify, and perform exploratory analysis from
any text data Who this book is forNatural Language Processing
Fundamentals is designed for novice and mid-level data scientists
and machine learning developers who want to gather and analyze text
data to build an NLP-powered product. It'll help you to have prior
experience of coding in Python using data types, writing functions,
and importing libraries. Some experience with linguistics and
probability is useful but not necessary.
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