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Présentation Data Science Format Broché
- Livre
Résumé : Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You'll be able to: Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Na?ve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...
Biographie:
Vijay Kotu is Vice President of Analytics at ServiceNow. He leads the implementation of large-scale data platforms and services to support the company's enterprise business. He has led analytics organizations for over a decade with focus on data strategy, business intelligence, machine learning, experimentation, engineering, enterprise adoption, and building analytics talent. Prior to joining ServiceNow, he was Vice President of Analytics at Yahoo. He worked at Life Technologies and Adteractive where he led marketing analytics, created algorithms to optimize online purchasing behavior, and developed data platforms to manage marketing campaigns. He is a member of the Association of Computing Machinery and a member of the Advisory Board at RapidMiner....
Sommaire:
1. Introduction
2. Data Science Process
3. Data Exploration
4. Classification
5. Deep Learning
6. Regression Methods
7. Association Analysis
8. Recommendation Engines
9. Clustering
10. Text Mining (renamed to: Natural Language Processing)
11. Time Series Forecasting
12. Anomaly Detection
13. Feature Selection
14. Model Evaluation
15. Efficient Model Execution
16. Getting Started with RapidMiner...
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