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About Machine Learning for Emotion Analysis
Kickstart your emotion analysis journey with this hands on step by step guide to data science successKey FeaturesDiscover the ins and outs of the end to end emotional analysis workflowExplore the use of various ML models to derive meaningful insights from all sorts of dataHone your craft by building and tweaking complex emotion analysis models in practical projectsBook DescriptionThe 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 learnDistinguish between sentiment analysis and emotion analysisMaster the art of data preprocessing and ensure high quality inputExpand your use of data sources through data transformationBuild models that employ cutting edge deep learning techniquesDiscover how best to tune your models hyperparametersExplore the use of KNN SVM and DNNs for advanced use casesBuild APIs and integrate your models into existing solutionsPractice your new skills by working on real world scenariosWho This Book Is ForThis 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 Table of ContentsFoundationsPreprocessing Constructing a DatasetDataset Quality Other Data Sources Model 1 KNNModel 2 SVMModel 3 DNNModel 4 Conditional ProbabilitiesResults and Next StepsUse case 1 A Hotel Review SystemUse case 2 Financial Trading
Detailed Information
| Author: | Dr. Tariq Ahmad |
|---|---|
| Publication Year: | 2023 |
| ISBN: | 1803240687 |
| Pages: | 334 |
| Language: | English |
| File Size: | 4.8 |
| Format: | |
| Price: | FREE |
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