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Explore the adoption of chatbots in business by focusing on the
design, deployment, and continuous improvement of chatbots in a
business, with a single use-case from the banking and insurance
sector. This book starts by identifying the business processes in
the banking and insurance industry. This involves data collection
from sources such as conversations from customer service centers,
online chats, emails, and other NLP sources. You'll then design the
solution architecture of the chatbot. Once the architecture is
framed, the author goes on to explain natural language
understanding (NLU), natural language processing (NLP), and natural
language generation (NLG) with examples. In the next sections,
you'll design and implement the backend framework of a typical
chatbot from scratch. You will also explore some popular
open-source chatbot frameworks such as Dialogflow and LUIS. The
authors then explain how you can integrate various third-party
services and enterprise databases with the custom chatbot
framework. In the final section, you'll discuss how to deploy the
custom chatbot framework on the AWS cloud. By the end of Building
an Enterprise Chatbot, you will be able to design and develop an
enterprise-ready conversational chatbot using an open source
development platform to serve the end user. What You Will Learn
Identify business processes where chatbots could be used Focus on
building a chatbot for one industry and one use-case rather than
building a ubiquitous and generic chatbot Design the solution
architecture for a chatbot Integrate chatbots with internal data
sources using APIs Discover the differences between natural
language understanding (NLU), natural language processing (NLP),
and natural language generation (NLG) Work with deployment and
continuous improvement through representational learning Who This
Book Is ForData scientists and enterprise architects who are
currently looking to deploy chatbot solutions to their business.
Examine the latest technological advancements in building a
scalable machine-learning model with big data using R. This second
edition shows you how to work with a machine-learning algorithm and
use it to build a ML model from raw data. You will see how to use R
programming with TensorFlow, thus avoiding the effort of learning
Python if you are only comfortable with R. As in the first edition,
the authors have kept the fine balance of theory and application of
machine learning through various real-world use-cases which gives
you a comprehensive collection of topics in machine learning. New
chapters in this edition cover time series models and deep
learning. What You'll Learn Understand machine learning algorithms
using R Master the process of building machine-learning models
Cover the theoretical foundations of machine-learning algorithms
See industry focused real-world use cases Tackle time series
modeling in R Apply deep learning using Keras and TensorFlow in R
Who This Book is For Data scientists, data science professionals,
and researchers in academia who want to understand the nuances of
machine-learning approaches/algorithms in practice using R.
Learn the ropes of supervised machine learning with R by studying
popular real-world use-cases, and understand how it drives object
detection in driver less cars, customer churn, and loan default
prediction. Key Features Study supervised learning algorithms by
using real-world datasets Fine tune optimal parameters with
hyperparameter optimization Select the best algorithm using the
model evaluation framework Book DescriptionR provides excellent
visualization features that are essential for exploring data before
using it in automated learning. Applied Supervised Learning with R
helps you cover the complete process of employing R to develop
applications using supervised machine learning algorithms for your
business needs. The book starts by helping you develop your
analytical thinking to create a problem statement using business
inputs and domain research. You will then learn different
evaluation metrics that compare various algorithms, and later
progress to using these metrics to select the best algorithm for
your problem. After finalizing the algorithm you want to use, you
will study the hyperparameter optimization technique to fine-tune
your set of optimal parameters. To prevent you from overfitting
your model, a dedicated section will even demonstrate how you can
add various regularization terms. By the end of this book, you will
have the advanced skills you need for modeling a supervised machine
learning algorithm that precisely fulfills your business needs.
What you will learn Develop analytical thinking to precisely
identify a business problem Wrangle data with dplyr, tidyr, and
reshape2 Visualize data with ggplot2 Validate your supervised
machine learning model using k-fold Optimize hyperparameters with
grid and random search, and Bayesian optimization Deploy your model
on Amazon Web Services (AWS) Lambda with plumber Improve your
model's performance with feature selection and dimensionality
reduction Who this book is forThis book is specially designed for
novice and intermediate-level data analysts, data scientists, and
data engineers who want to explore different methods of supervised
machine learning and its various use cases. Some background in
statistics, probability, calculus, linear algebra, and programming
will help you thoroughly understand and follow the content of this
book.
Examine the latest technological advancements in building a
scalable machine learning model with Big Data using R. This book
shows you how to work with a machine learning algorithm and use it
to build a ML model from raw data. All practical demonstrations
will be explored in R, a powerful programming language and software
environment for statistical computing and graphics. The various
packages and methods available in R will be used to explain the
topics. For every machine learning algorithm covered in this book,
a 3-D approach of theory, case-study and practice will be given.
And where appropriate, the mathematics will be explained through
visualization in R. All the images are available in color and
hi-res as part of the code download. This new paradigm of teaching
machine learning will bring about a radical change in perception
for many of those who think this subject is difficult to learn.
Though theory sometimes looks difficult, especially when there is
heavy mathematics involved, the seamless flow from the theoretical
aspects to example-driven learning provided in this book makes it
easy for someone to connect the dots.. What You'll Learn Use the
model building process flow Apply theoretical aspects of machine
learning Review industry-based cae studies Understand ML algorithms
using R Build machine learning models using Apache Hadoop and Spark
Who This Book is For Data scientists, data science professionals
and researchers in academia who want to understand the nuances of
machine learning approaches/algorithms along with ways to see them
in practice using R. The book will also benefit the readers who
want to understand the technology behind implementing a scalable
machine learning model using Apache Hadoop, Hive, Pig and Spark.
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