R: Unleash Machine Learning Techniques

R: Unleash Machine Learning Techniques
  • eBook:
    R: Unleash Machine Learning Techniques
  • Author:
    Raghav Bali, Dipanjan Sarkar, Brett Lantz
  • Edition:
  • Categories:
  • Data:
    June 19, 2017
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    1140 pages
  • Format:
    PDF, ePUB

Book Description
This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R.


Module 1: R Machine Learning By Example
Chapter 1: Getting Started with R and Machine Learning
Chapter 2: Let's Help Machines Learn
Chapter 3: Predicting Customer Shopping Trends with Market Basket Analysis
Chapter 4: Building a Product Recommendation System
Chapter 5: Credit Risk Detection and Prediction – Descriptive Analytics
Chapter 6: Credit Risk Detection and Prediction – Predictive Analytics
Chapter 7: Social Media Analysis – Analyzing Twitter Data
Chapter 8: Sentiment Analysis of Twitter Data

Module 2: Machine Learning with R
Chapter 1: Introducing Machine Learning
Chapter 2: Managing and Understanding Data
Chapter 3: Lazy Learning – Classification Using Nearest Neighbors
Chapter 4: Probabilistic Learning – Classification Using Naive Bayes
Chapter 5: Divide and Conquer – Classification Using Decision Trees and Rules
Chapter 6: Forecasting Numeric Data – Regression Methods
Chapter 7: Black Box Methods – Neural Networks and Support Vector Machines
Chapter 8: Finding Patterns – Market Basket Analysis Using Association Rules
Chapter 9: Finding Groups of Data – Clustering with k-means
Chapter 10: Evaluating Model Performance
Chapter 11: Improving Model Performance
Chapter 12: Specialized Machine Learning Topics

Module 3: Mastering Machine Learning with R
Chapter 1: A Process for Success
Chapter 2: Linear Regression – The Blocking and Tackling of Machine Learning
Chapter 3: Logistic Regression and Discriminant Analysis
Chapter 4: Advanced Feature Selection in Linear Models
Chapter 5: More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
Chapter 6: Classification and Regression Trees
Chapter 7: Neural Networks
Chapter 8: Cluster Analysis
Chapter 9: Principal Components Analysis
Chapter 10: Market Basket Analysis and Recommendation Engines
Chapter 11: Time Series and Causality
Chapter 12: Text Mining
Chapter 13: R Fundamentals

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