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This book is a comprehensive, step-by-step and one-stop guide for
the Java SE 8 Programmer II exam (IZ0-809). Salient features of
this book include: 100% coverage of the exam topics, a full-length
mock exam, practice exam questions, exam notes and tips. Oracle
Certified Professional Java SE 8 Programmer II Guide (Exam IZ0-809)
is a comprehensive guide for the OCPJP 8 exam. The book starts by
answering frequently asked questions about the OCPJP 8 exam
(Chapter 1). The book maps each exam topic into a chapter and
covers 100% of the exam topics (next 12 Chapters). Exam topics are
discussed using numerous programming and real-world examples. Each
chapter ends with practice exam questions and a quick summary that
revises key concepts covered in the chapter from exam perspective.
After reading the main chapters, you can take the full-length mock
exam to ensure that you have enough practice before actually taking
the exam (Chapter 14). If you are an OCPJP 8 exam aspirant, this
book is certainly for you. This book assumes that you are already
familiar with Java fundamentals (that is in line with the
prerequisite of having a OCAJP 8 certification before you take up
the OCPJP 8 exam). This book will be a delectable read to you
because of its simple language, example driven approach,
easy-to-read style, and complete focus towards the exam. Salient
Features * In-depth and 100% coverage of all 12 exam topics for the
certification * Numerous illustrative programming and real-world
examples* Hundreds of practice exam questions (including a
full-length mock exam) What you will learn: * Have the necessary
knowledge to clear the exam since 100% of the exam topics are
covered to the required depth * clearly understand the scope and
objectives of the exam, the technical topics covered in the exam,
and type and level-of-difficulty of the exam questions (in short,
you will clearly know what's exactly required for passing the exam)
* get into an "exam mindset" by trying out hundreds of practice
exam questions.
Nanotechnology for CO2 Utilization in Oilfield Applications
delivers a critical reference for petroleum and reservoir engineers
to learn the latest advancements of combining the use of CO2 and
nanofluids to lower carbon footprint. Starting with the existing
chemical and physical methods employed for synthesizing nanofluids,
the reference moves into the scalability and fabrication techniques
given for all the various nanofluids currently used in oilfield
applications. This is followed by various, relevant
characterization techniques. Advancing on, the reference covers
nanofluids used in drilling, cementing, and EOR fluids, including
their challenges and implementation problems associated with the
use of nanofluids. Finally, the authors discuss the combined
application of CO2 and nanofluids, listing challenges and benefits
of CO2, such as carbonation capacity of nanofluids via rheological
analysis for better CO2 utilization. Supported by visual world maps
on CCS sites and case studies across the industry, this book gives
today's engineers a much-needed tool to lower emissions.
Master the essential skills needed to recognize and solve complex
problems with machine learning and deep learning. Using real-world
examples that leverage the popular Python machine learning
ecosystem, this book is your perfect companion for learning the art
and science of machine learning to become a successful
practitioner. The concepts, techniques, tools, frameworks, and
methodologies used in this book will teach you how to think,
design, build, and execute machine learning systems and projects
successfully. Practical Machine Learning with Python follows a
structured and comprehensive three-tiered approach packed with
hands-on examples and code. Part 1 focuses on understanding machine
learning concepts and tools. This includes machine learning basics
with a broad overview of algorithms, techniques, concepts and
applications, followed by a tour of the entire Python machine
learning ecosystem. Brief guides for useful machine learning tools,
libraries and frameworks are also covered. Part 2 details standard
machine learning pipelines, with an emphasis on data processing
analysis, feature engineering, and modeling. You will learn how to
process, wrangle, summarize and visualize data in its various
forms. Feature engineering and selection methodologies will be
covered in detail with real-world datasets followed by model
building, tuning, interpretation and deployment. Part 3 explores
multiple real-world case studies spanning diverse domains and
industries like retail, transportation, movies, music, marketing,
computer vision and finance. For each case study, you will learn
the application of various machine learning techniques and methods.
The hands-on examples will help you become familiar with
state-of-the-art machine learning tools and techniques and
understand what algorithms are best suited for any problem.
Practical Machine Learning with Python will empower you to start
solving your own problems with machine learning today! What You'll
Learn Execute end-to-end machine learning projects and systems
Implement hands-on examples with industry standard, open source,
robust machine learning tools and frameworks Review case studies
depicting applications of machine learning and deep learning on
diverse domains and industries Apply a wide range of machine
learning models including regression, classification, and
clustering. Understand and apply the latest models and
methodologies from deep learning including CNNs, RNNs, LSTMs and
transfer learning. Who This Book Is For IT professionals, analysts,
developers, data scientists, engineers, graduate students
Oracle Certified Professional Java SE 7 Programmer Exams 1Z0-804
and 1Z0-805 is a concise, comprehensive, step-by-step, and one-stop
guide for the Oracle Certified Professional Java SE 7 Programmer
Exam. The first two chapters set the stage for exam preparation and
let the reader get started quickly. The first chapter answers
frequently asked questions about the OCPJP exam. This book assumes
that the reader is already familiar with Java fundamentals which is
in line with the prerequisite of having a OCAJP certification. The
book sports considerable supportive material to help the reader in
effective exam preparation in the form of appendices: *2 mock tests
to give the reader a sense of a real-exam. * An instant refresher
summarizing the most important concepts (with tips on answering
questions) to revise just before the exam. * An API quick reference
covering only the most important classes and methods relevant to
the exam topics. This book will be a delectable read for any OCPJP
aspirant because of its simple language, example driven approach,
and easy-to-read style.Further, given its 100% focus on the exam
and helpful supportive material, this book is clearly an attractive
buy to OCPJP aspirants worldwide. What you'll learn * In-depth
coverage of all 13 exam topics for the certification. * The book
covers programming concepts succinctly with numerous illustrative
programming and real-world examples. These examples will help the
reader quickly internalize the discussed concepts. * The reader
will clearly understand the scope and objectives of the exam, the
technical topics covered in the exam, and type and
level-of-difficulty of the exam questions. With this, the reader
will clearly know what's exactly required for passing the exam. *
Review questions tune the reader to an exam mindset. By making
mistakes and reading the detailed explanations for the answers, the
reader will be better prepared for getting certified. Attempting
the practice questions and mock test will help the reader gain
necessary confidence to clear the exam. Who this book is for This
book is targeted primarily at students and programmers who want to
crack the OCPJP exam. Trainers and teachers can use this book as
training material for OCPJP exam preparation.This book is also
useful to readers who want to refresh their knowledge in Java
programming or gain better understanding on using various Java
APIs.
Awareness of design smells - indicators of common design problems -
helps developers or software engineers understand mistakes made
while designing, what design principles were overlooked or
misapplied, and what principles need to be applied properly to
address those smells through refactoring. Developers and software
engineers may "know" principles and patterns, but are not aware of
the "smells" that exist in their design because of wrong or
mis-application of principles or patterns. These smells tend to
contribute heavily to technical debt - further time owed to fix
projects thought to be complete - and need to be addressed via
proper refactoring. Refactoring for Software Design Smells presents
25 structural design smells, their role in identifying design
issues, and potential refactoring solutions. Organized across
common areas of software design, each smell is presented with
diagrams and examples illustrating the poor design practices and
the problems that result, creating a catalog of nuggets of readily
usable information that developers or engineers can apply in their
projects. The authors distill their research and experience as
consultants and trainers, providing insights that have been used to
improve refactoring and reduce the time and costs of managing
software projects. Along the way they recount anecdotes from actual
projects on which the relevant smell helped address a design issue.
Tap into the realm of social media and unleash the power of
analytics for data-driven insights using R About This Book * A
practical guide written to help leverage the power of the R
eco-system to extract, process, analyze, visualize and model social
media data * Learn about data access, retrieval, cleaning, and
curation methods for data originating from various social media
platforms. * Visualize and analyze data from social media platforms
to understand and model complex relationships using various
concepts and techniques such as Sentiment Analysis, Topic Modeling,
Text Summarization, Recommendation Systems, Social Network
Analysis, Classification, and Clustering. Who This Book Is For It
is targeted at IT professionals, Data Scientists, Analysts,
Developers, Machine Learning Enthusiasts, social media marketers
and anyone with a keen interest in data, analytics, and generating
insights from social data. Some background experience in R would be
helpful, but not necessary, since this book is written keeping in
mind, that readers can have varying levels of expertise. What You
Will Learn * Learn how to tap into data from diverse social media
platforms using the R ecosystem * Use social media data to
formulate and solve real-world problems * Analyze user social
networks and communities using concepts from graph theory and
network analysis * Learn to detect opinion and sentiment, extract
themes, topics, and trends from unstructured noisy text data from
diverse social media channels * Understand the art of representing
actionable insights with effective visualizations * Analyze data
from major social media channels such as Twitter, Facebook, Flickr,
Foursquare, Github, StackExchange, and so on * Learn to leverage
popular R packages such as ggplot2, topicmodels, caret, e1071, tm,
wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In
Detail The Internet has truly become humongous, especially with the
rise of various forms of social media in the last decade, which
give users a platform to express themselves and also communicate
and collaborate with each other. This book will help the reader to
understand the current social media landscape and to learn how
analytics can be leveraged to derive insights from it. This data
can be analyzed to gain valuable insights into the behavior and
engagement of users, organizations, businesses, and brands. It will
help readers frame business problems and solve them using social
data. The book will also cover several practical real-world use
cases on social media using R and its advanced packages to utilize
data science methodologies such as sentiment analysis, topic
modeling, text summarization, recommendation systems, social
network analysis, classification, and clustering. This will enable
readers to learn different hands-on approaches to obtain data from
diverse social media sources such as Twitter and Facebook. It will
also show readers how to establish detailed workflows to process,
visualize, and analyze data to transform social data into
actionable insights. Style and approach This book follows a
step-by-step approach with detailed strategies for understanding,
extracting, analyzing, visualizing, and modeling data from several
major social network platforms such as Facebook, Twitter,
Foursquare, Flickr, Github, and StackExchange. The chapters cover
several real-world use cases and leverage data science, machine
learning, network analysis, and graph theory concepts along with
the R ecosystem, including popular packages such as ggplot2,
caret,dplyr, topicmodels, tm, and so on.
"Consumer Behaviour towards durables in Chandigarh" studies how
people buy durable goods, what they buy, when they buy, why they
buy and where do they prefer to buy. The study provides useful
insights to marketers and manufacturers in developing strategies
for product development, pricing, positioning, promoting,
distributing and creating brand loyalty among consumers. The study
also displays application of wide range of tools and models for
academic use. The study develops a deeper understanding towards the
topic of consumer behaviour by applying and summarizing various
models of consumer behaviour such as Economic model, learning
model, psycho-analytical model and sociological model. Televisions
were selected as durable for the study. The study mainly analyses,
T.V. sets owned by the respondents, factors motivating them to
purchase T.V. sets, role of various factors in purchase decisions,
brand selection, store selection, influence of family members etc.
Study was conducted at Chandigarh - The city Beautiful which is
located at North western part of India and is a dynamic,
cosmopolitan and ever expanding market of consumer durables.
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