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Machine Learning for Emotion Analysis - Understand the emotion behind every story (Paperback)
Loot Price: R1,234
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Machine Learning for Emotion Analysis - Understand the emotion behind every story (Paperback)
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Kickstart your emotion analysis journey with this hands-on,
step-by-step guide to data science success Key Features * Discover
the ins and outs of the end-to-end emotional analysis workflow *
Explore the use of various ML models to derive meaningful insights
from all sorts of data * Hone your craft by building and tweaking
complex emotion analysis models in practical projects Book
Description The AI winter has long thawed, but many organizations
are still failing to harness the power of machine learning (ML). If
you want to tap that potential and add value to your own business
with cutting-edge emotion analysis, you've found what you need in
this trusty guide. In Machine Learning for Emotion Analysis, you'll
take your foundational data science skills and grow them in the
exciting realm of emotion analysis. With its practical approach,
you'll be equipped with everything you need to give your company a
clear insight into what your customers are thinking. This
no-nonsense guide jumps right into the practicalities of emotion
analysis, teaching you how to preprocess data, build a serviceable
dataset, and ensure top-notch data quality. Once you're set up for
success, we get hands-on with complex ML techniques. This is where
you go from the intermediate to the advanced, covering deep neural
networks, support vector machines, conditional probabilities, and
more, as you experience the full breadth of possibilities with
emotion analysis. The book finally rounds out with a couple of
in-depth use cases - a sort of sandbox for you to experiment with
your newly acquired skill set. By the end of this book, you'll be
ready to present yourself as a valuable asset to any organization
that takes data science seriously. What you will learn *
Distinguish between sentiment analysis and emotion analysis *
Master the art of data preprocessing and ensure high-quality input
* Expand your use of data sources through data transformation *
Build models that employ cutting-edge deep learning techniques *
Discover how best to tune your models' hyperparameters * Explore
the use of KNN, SVM, and DNNs for advanced use cases * Build APIs
and integrate your models into existing solutions * Practice your
new skills by working on real-world scenarios Who This Book Is For
This book is for data scientists and Python developers who want to
gain insights into what people are saying about their product,
company, brand, governorship, and more. Basic knowledge of machine
learning and Python programming knowledge is necessary to grasp the
concepts covered.
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