Description

Book Synopsis


Table of Contents
Part I: Introduction 1. Overview of Data Science, Analytics, and Machine Learning 2. Introduction to the R Language Part II: Applied Statistics and Data Visualization 3. Variables and Measurement Scales 4. Descriptive and Probabilistic Statistics 5. Hypotheses Tests 6. Data Visualization and Multivariate Graphs Part III: Data Mining and Preparation 7. Building Handcrafted Robots 8. Using APIs to Collect Data 9. Managing Data Part IV: Unsupervised Machine Learning Techniques 10. Cluster Analysis 11. Factorial and Principal Component Analysis (PCA) 12. Association Rules and Correspondence Analysis Part V: Supervised Machine Learning Techniques 13. Simple and Multiple Regression Analysis 14. Binary, Ordinal and Multinomial Regression Analysis 15. Count-Data and Zero-Inflated Regression Analysis 16. Generalized Linear Mixed Models Part VI: Improving Performance and Introduction to Deep Learning 17. Support Vector Machine 18. CART (Classification and Regression Trees) 19. Bagging, Boosting and Uplift (Persuasion) Modeling 20. Random Forest 21. Artificial Neural Network 22. Introduction to Deep Learning Part VII: Spatial Analysis 23. Working on Shapefiles 24. Dealing with Simple Features Objects 25. Raster Objects 26. Exploratory Spatial Analysis Part VII: Adding Value to your Work 27. Enhanced and Interactive Graphs 28. Dashboards with R

Data Science Analytics and Machine Learning with

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A Paperback by Luiz Paulo Favero, Patricia Belfiore, Rafael de Freitas Souza

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    View other formats and editions of Data Science Analytics and Machine Learning with by Luiz Paulo Favero

    Publisher: Elsevier Science
    Publication Date: 1/25/2023 12:00:00 AM
    ISBN13: 9780128242711, 978-0128242711
    ISBN10: 012824271X
    Also in:
    Computing Machine learning

    Description

    Book Synopsis


    Table of Contents
    Part I: Introduction 1. Overview of Data Science, Analytics, and Machine Learning 2. Introduction to the R Language Part II: Applied Statistics and Data Visualization 3. Variables and Measurement Scales 4. Descriptive and Probabilistic Statistics 5. Hypotheses Tests 6. Data Visualization and Multivariate Graphs Part III: Data Mining and Preparation 7. Building Handcrafted Robots 8. Using APIs to Collect Data 9. Managing Data Part IV: Unsupervised Machine Learning Techniques 10. Cluster Analysis 11. Factorial and Principal Component Analysis (PCA) 12. Association Rules and Correspondence Analysis Part V: Supervised Machine Learning Techniques 13. Simple and Multiple Regression Analysis 14. Binary, Ordinal and Multinomial Regression Analysis 15. Count-Data and Zero-Inflated Regression Analysis 16. Generalized Linear Mixed Models Part VI: Improving Performance and Introduction to Deep Learning 17. Support Vector Machine 18. CART (Classification and Regression Trees) 19. Bagging, Boosting and Uplift (Persuasion) Modeling 20. Random Forest 21. Artificial Neural Network 22. Introduction to Deep Learning Part VII: Spatial Analysis 23. Working on Shapefiles 24. Dealing with Simple Features Objects 25. Raster Objects 26. Exploratory Spatial Analysis Part VII: Adding Value to your Work 27. Enhanced and Interactive Graphs 28. Dashboards with R

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