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Gain a working knowledge of prescriptive AI, its history, and its
current and future trends. This book will help you evaluate
different AI-driven predictive analytics techniques and help you
incorporate decision intelligence into your business workflow
through real-world examples. The book kicks off with an
introduction to decision intelligence and provides insight into
prescriptive AI and how it can be woven into various business
strategies and frameworks. You'll then be introduced to different
decision intelligence methodologies and how to implement them,
along with advantages and limitations of each. Digging deeper, the
authors then walk you through how to perform simulations and
interpret the results. A full chapter is devoted to embedding
decision intelligence processes and outcomes into your business
workflow using various applications. The book concludes by
exploring different cognitive biases humans are prone to, and how
those biases can be eliminated by combining machine and human
intelligence. Upon completing this book, you will understand
prescriptive AI, tools, and techniques and will be ready to
incorporate them into your business workflow. What You Will Learn
Implement full-fledged decision intelligence applications using
Python Leverage the tools, techniques, and methodologies for
prescriptive AI Understand how prescriptive AI can be used in
different domains through practical examples Interpret results and
integrate them into your decision making Who This Book Is ForData
Scientists and Machine Learning Engineers, as well as business
professionals who want to understand how AI-driven decision
intelligence can help grow their business.
This book teaches the practical implementation of various concepts
for time series analysis and modeling with Python through
problem-solution-style recipes, starting with data reading and
preprocessing. It begins with the fundamentals of time series
forecasting using statistical modeling methods like AR
(autoregressive), MA (moving-average), ARMA (autoregressive
moving-average), and ARIMA (autoregressive integrated
moving-average). Next, you'll learn univariate and multivariate
modeling using different open-sourced packages like Fbprohet, stats
model, and sklearn. You'll also gain insight into classic machine
learning-based regression models like randomForest, Xgboost, and
LightGBM for forecasting problems. The book concludes by
demonstrating the implementation of deep learning models (LSTMs and
ANN) for time series forecasting. Each chapter includes several
code examples and illustrations. After finishing this book, you
will have a foundational understanding of various concepts relating
to time series and its implementation in Python. What You Will
Learn Implement various techniques in time series analysis using
Python. Utilize statistical modeling methods such as AR
(autoregressive), MA (moving-average), ARMA (autoregressive
moving-average) and ARIMA (autoregressive integrated
moving-average) for time series forecasting Understand univariate
and multivariate modeling for time series forecasting Forecast
using machine learning and deep learning techniques such as GBM and
LSTM (long short-term memory) Who This Book Is ForData Scientists,
Machine Learning Engineers, and software developers interested in
time series analysis.
Focus on implementing end-to-end projects using Python and leverage
state-of-the-art algorithms. This book teaches you to efficiently
use a wide range of natural language processing (NLP) packages to:
implement text classification, identify parts of speech, utilize
topic modeling, text summarization, sentiment analysis, information
retrieval, and many more applications of NLP. The book begins with
text data collection, web scraping, and the different types of data
sources. It explains how to clean and pre-process text data, and
offers ways to analyze data with advanced algorithms. You then
explore semantic and syntactic analysis of the text. Complex NLP
solutions that involve text normalization are covered along with
advanced pre-processing methods, POS tagging, parsing, text
summarization, sentiment analysis, word2vec, seq2seq, and much
more. The book presents the fundamentals necessary for applications
of machine learning and deep learning in NLP. This second edition
goes over advanced techniques to convert text to features such as
Glove, Elmo, Bert, etc. It also includes an understanding of how
transformers work, taking sentence BERT and GPT as examples. The
final chapters explain advanced industrial applications of NLP with
solution implementation and leveraging the power of deep learning
techniques for NLP problems. It also employs state-of-the-art
advanced RNNs, such as long short-term memory, to solve complex
text generation tasks. After reading this book, you will have a
clear understanding of the challenges faced by different industries
and you will have worked on multiple examples of implementing NLP
in the real world. What You Will Learn Know the core concepts of
implementing NLP and various approaches to natural language
processing (NLP), including NLP using Python libraries such as
NLTK, textblob, SpaCy, Standford CoreNLP, and more Implement text
pre-processing and feature engineering in NLP, including advanced
methods of feature engineering Understand and implement the
concepts of information retrieval, text summarization, sentiment
analysis, text classification, and other advanced NLP techniques
leveraging machine learning and deep learning Who This Book Is For
Data scientists who want to refresh and learn various concepts of
natural language processing (NLP) through coding exercises
Design and develop end-to-end, production-grade computer vision
projects for real-world industry problems. This book discusses
computer vision algorithms and their applications using PyTorch.
The book begins with the fundamentals of computer vision:
convolutional neural nets, RESNET, YOLO, data augmentation, and
other regularization techniques used in the industry. And then it
gives you a quick overview of the PyTorch libraries used in the
book. After that, it takes you through the implementation of image
classification problems, object detection techniques, and transfer
learning while training and running inference. The book covers
image segmentation and an anomaly detection model. And it discusses
the fundamentals of video processing for computer vision tasks
putting images into videos. The book concludes with an explanation
of the complete model building process for deep learning frameworks
using optimized techniques with highlights on model AI
explainability. After reading this book, you will be able to build
your own computer vision projects using transfer learning and
PyTorch. What You Will Learn Solve problems in computer vision with
PyTorch. Implement transfer learning and perform image
classification, object detection, image segmentation, and other
computer vision applications Design and develop production-grade
computer vision projects for real-world industry problems Interpret
computer vision models and solve business problems Who This Book Is
For Data scientists and machine learning engineers interested in
building computer vision projects and solving business problems
This book will teach you how to build recommender systems with
machine learning algorithms using Python. Recommender systems have
become an essential part of every internet-based business today.
You'll start by learning basic concepts of recommender systems,
with an overview of different types of recommender engines and how
they function. Next, you will see how to build recommender systems
with traditional algorithms such as market basket analysis and
content- and knowledge-based recommender systems with NLP. The
authors then demonstrate techniques such as collaborative filtering
using matrix factorization and hybrid recommender systems that
incorporate both content-based and collaborative filtering
techniques. This is followed by a tutorial on building machine
learning-based recommender systems using clustering and
classification algorithms like K-means and random forest. The last
chapters cover NLP, deep learning, and graph-based techniques to
build a recommender engine. Each chapter includes data preparation,
multiple ways to evaluate and optimize the recommender systems,
supporting examples, and illustrations. By the end of this book,
you will understand and be able to build recommender systems with
various tools and techniques with machine learning, deep learning,
and graph-based algorithms. What You Will Learn Understand and
implement different recommender systems techniques with Python
Employ popular methods like content- and knowledge-based,
collaborative filtering, market basket analysis, and matrix
factorization Build hybrid recommender systems that incorporate
both content-based and collaborative filtering Leverage machine
learning, NLP, and deep learning for building recommender systems
Who This Book Is ForData scientists, machine learning engineers,
and Python programmers interested in building and implementing
recommender systems to solve problems.
Leverage machine learning and deep learning techniques to build
fully-fledged natural language processing (NLP) projects. Projects
throughout this book grow in complexity and showcase methodologies,
optimizing tips, and tricks to solve various business problems. You
will use modern Python libraries and algorithms to build end-to-end
NLP projects. The book starts with an overview of natural language
processing (NLP) and artificial intelligence to provide a quick
refresher on algorithms. Next, it covers end-to-end NLP projects
beginning with traditional algorithms and projects such as customer
review sentiment and emotion detection, topic modeling, and
document clustering. From there, it delves into e-commerce related
projects such as product categorization using the description of
the product, a search engine to retrieve the relevant content, and
a content-based recommendation system to enhance user experience.
Moving forward, it explains how to build systems to find similar
sentences using contextual embedding, summarizing huge documents
using recurrent neural networks (RNN), automatic word suggestion
using long short-term memory networks (LSTM), and how to build a
chatbot using transfer learning. It concludes with an exploration
of next-generation AI and algorithms in the research space. By the
end of this book, you will have the knowledge needed to solve
various business problems using NLP techniques. What You Will Learn
Implement full-fledged intelligent NLP applications with Python
Translate real-world business problem on text data with NLP
techniques Leverage machine learning and deep learning techniques
to perform smart language processing Gain hands-on experience
implementing end-to-end search engine information retrieval, text
summarization, chatbots, text generation, document clustering and
product classification, and more Who This Book Is For Data
scientists, machine learning engineers, and deep learning
professionals looking to build natural language applications using
Python
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