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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
Leverage Natural Language Processing (NLP) in Python and learn how
to set up your own robust environment for performing text
analytics. This second edition has gone through a major revamp and
introduces several significant changes and new topics based on the
recent trends in NLP. You'll see how to use the latest
state-of-the-art frameworks in NLP, coupled with machine learning
and deep learning models for supervised sentiment analysis powered
by Python to solve actual case studies. Start by reviewing Python
for NLP fundamentals on strings and text data and move on to
engineering representation methods for text data, including both
traditional statistical models and newer deep learning-based
embedding models. Improved techniques and new methods around
parsing and processing text are discussed as well. Text
summarization and topic models have been overhauled so the book
showcases how to build, tune, and interpret topic models in the
context of an interest dataset on NIPS conference papers.
Additionally, the book covers text similarity techniques with a
real-world example of movie recommenders, along with sentiment
analysis using supervised and unsupervised techniques. There is
also a chapter dedicated to semantic analysis where you'll see how
to build your own named entity recognition (NER) system from
scratch. While the overall structure of the book remains the same,
the entire code base, modules, and chapters has been updated to the
latest Python 3.x release. What You'll Learn * Understand NLP and
text syntax, semantics and structure* Discover text cleaning and
feature engineering* Review text classification and text clustering
* Assess text summarization and topic models* Study deep learning
for NLP Who This Book Is For IT professionals, data analysts,
developers, linguistic experts, data scientists and engineers and
basically anyone with a keen interest in linguistics, analytics and
generating insights from textual data.
Deep learning simplified by taking supervised, unsupervised, and
reinforcement learning to the next level using the Python ecosystem
Key Features Build deep learning models with transfer learning
principles in Python implement transfer learning to solve
real-world research problems Perform complex operations such as
image captioning neural style transfer Book DescriptionTransfer
learning is a machine learning (ML) technique where knowledge
gained during training a set of problems can be used to solve other
similar problems. The purpose of this book is two-fold; firstly, we
focus on detailed coverage of deep learning (DL) and transfer
learning, comparing and contrasting the two with easy-to-follow
concepts and examples. The second area of focus is real-world
examples and research problems using TensorFlow, Keras, and the
Python ecosystem with hands-on examples. The book starts with the
key essential concepts of ML and DL, followed by depiction and
coverage of important DL architectures such as convolutional neural
networks (CNNs), deep neural networks (DNNs), recurrent neural
networks (RNNs), long short-term memory (LSTM), and capsule
networks. Our focus then shifts to transfer learning concepts, such
as model freezing, fine-tuning, pre-trained models including VGG,
inception, ResNet, and how these systems perform better than DL
models with practical examples. In the concluding chapters, we will
focus on a multitude of real-world case studies and problems
associated with areas such as computer vision, audio analysis and
natural language processing (NLP). By the end of this book, you
will be able to implement both DL and transfer learning principles
in your own systems. What you will learn Set up your own DL
environment with graphics processing unit (GPU) and Cloud support
Delve into transfer learning principles with ML and DL models
Explore various DL architectures, including CNN, LSTM, and capsule
networks Learn about data and network representation and loss
functions Get to grips with models and strategies in transfer
learning Walk through potential challenges in building complex
transfer learning models from scratch Explore real-world research
problems related to computer vision and audio analysis Understand
how transfer learning can be leveraged in NLP Who this book is
forHands-On Transfer Learning with Python is for data scientists,
machine learning engineers, analysts and developers with an
interest in data and applying state-of-the-art transfer learning
methodologies to solve tough real-world problems. Basic proficiency
in machine learning and Python is required.
Find out how to build smarter machine learning systems with R.
Follow this three module course to become a more fluent machine
learning practitioner. About This Book * Build your confidence with
R and find out how to solve a huge range of data-related problems *
Get to grips with some of the most important machine learning
techniques being used by data scientists and analysts across
industries today * Don't just learn - apply your knowledge by
following featured practical projects covering everything from
financial modeling to social media analysis Who This Book Is For
Aimed for intermediate-to-advanced people (especially data
scientist) who are already into the field of data science What You
Will Learn * Get to grips with R techniques to clean and prepare
your data for analysis, and visualize your results * Implement R
machine learning algorithms from scratch and be amazed to see the
algorithms in action * Solve interesting real-world problems using
machine learning and R as the journey unfolds * Write reusable code
and build complete machine learning systems from the ground up *
Learn specialized machine learning techniques for text mining,
social network data, big data, and more * Discover the different
types of machine learning models and learn which is best to meet
your data needs and solve your analysis problems * Evaluate and
improve the performance of machine learning models * Learn
specialized machine learning techniques for text mining, social
network data, big data, and more In Detail R is the established
language of data analysts and statisticians around the world. And
you shouldn't be afraid to use it... This Learning Path will take
you through the fundamentals of R and demonstrate how to use the
language to solve a diverse range of challenges through machine
learning. Accessible yet comprehensive, it provides you with
everything you need to become more a more fluent data professional,
and more confident with R. In the first module you'll get to grips
with the fundamentals of R. This means you'll be taking a look at
some of the details of how the language works, before seeing how to
put your knowledge into practice to build some simple machine
learning projects that could prove useful for a range of real world
problems. For the following two modules we'll begin to investigate
machine learning algorithms in more detail. To build upon the
basics, you'll get to work on three different projects that will
test your skills. Covering some of the most important algorithms
and featuring some of the most popular R packages, they're all
focused on solving real problems in different areas, ranging from
finance to social media. This Learning Path has been curated from
three Packt products: * R Machine Learning By Example By Raghav
Bali, Dipanjan Sarkar * Machine Learning with R Learning - Second
Edition By Brett Lantz * Mastering Machine Learning with R By Cory
Lesmeister Style and approach This is an enticing learning path
that starts from the very basics to gradually pick up pace as the
story unfolds. Each concept is first defined in the larger context
of things succinctly, followed by a detailed explanation of their
application. Each topic is explained with the help of a project
that solves a real-world problem involving hands-on work thus
giving you a deep insight into the world of machine learning.
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.
Understand the fundamentals of machine learning with R and build
your own dynamic algorithms to tackle complicated real-world
problems successfully About This Book * Get to grips with the
concepts of machine learning through exciting real-world examples *
Visualize and solve complex problems by using power-packed R
constructs and its robust packages for machine learning * Learn to
build your own machine learning system with this example-based
practical guide Who This Book Is For If you are interested in
mining useful information from data using state-of-the-art
techniques to make data-driven decisions, this is a go-to guide for
you. No prior experience with data science is required, although
basic knowledge of R is highly desirable. Prior knowledge in
machine learning would be helpful but is not necessary. What You
Will Learn * Utilize the power of R to handle data extraction,
manipulation, and exploration techniques * Use R to visualize data
spread across multiple dimensions and extract useful features *
Explore the underlying mathematical and logical concepts that drive
machine learning algorithms * Dive deep into the world of analytics
to predict situations correctly * Implement R machine learning
algorithms from scratch and be amazed to see the algorithms in
action * Write reusable code and build complete machine learning
systems from the ground up * Solve interesting real-world problems
using machine learning and R as the journey unfolds * Harness the
power of robust and optimized R packages to work on projects that
solve real-world problems in machine learning and data science In
Detail Data science and machine learning are some of the top
buzzwords in the technical world today. From retail stores to
Fortune 500 companies, everyone is working hard to making machine
learning give them data-driven insights to grow their business.
With powerful data manipulation features, machine learning
packages, and an active developer community, R empowers users to
build sophisticated machine learning systems to solve real-world
data problems. This book takes you on a data-driven journey that
starts with the very basics of R and machine learning and gradually
builds upon the concepts to work on projects that tackle real-world
problems. You'll begin by getting an understanding of the core
concepts and definitions required to appreciate machine learning
algorithms and concepts. Building upon the basics, you will then
work on three different projects to apply the concepts of machine
learning, following current trends and cover major algorithms as
well as popular R packages in detail. These projects have been
neatly divided into six different chapters covering the worlds of
e-commerce, finance, and social-media, which are at the very core
of this data-driven revolution. Each of the projects will help you
to understand, explore, visualize, and derive insights depending
upon the domain and algorithms. Through this book, you will learn
to apply the concepts of machine learning to deal with data-related
problems and solve them using the powerful yet simple language, R.
Style and approach The book is an enticing journey that starts from
the very basics to gradually pick up pace as the story unfolds.
Each concept is first defined in the larger context of things
succinctly, followed by a detailed explanation of their
application. Each topic is explained with the help of a project
that solves a real real-world problem involving hands-on work thus
giving you a deep insight into the world of machine learning.
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