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Oreilly - R Data Analytics Projects - 9781789536829
Oreilly - R Data Analytics Projects
by Dipankar Sarkar, Raghav Bali | Released May 2018 | ISBN: 9781789536829


Solve interesting real-world problems using machine learning and RAbout This VideoLearn to build your own machine learning system with this example-based practical guideGet to grips with machine learning concepts through exciting real-world examplesVisualize and solve complex problems by using power-packed R constructs and its robust packages for machine learningIn DetailWith powerful features and packages, R empowers users to build sophisticated machine learning systems to solve real-world data problems.This video course takes you on a data-driven journey that starts with the very basics of R and machine learning. You will then work on three different projects to apply the concepts of machine learning. Each project will help you to understand, explore, visualize, and derive domain- and algorithm-based insights.By the end of this course, you will have learned to apply the concepts of machine learning to data-related problems and solve them with help of R.All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/R-Data-Analytics-Projects Show and hide more
  1. Chapter 1 : Getting Started with R and Machine Learning
    • The Course Overview 00:03:26
    • Delving into the Basics of R 00:06:11
    • Data Structures in R 00:09:23
    • Lists and Data Frames 00:08:38
    • Working with Functions 00:04:15
    • Controlling Code Flow 00:03:30
    • Advanced Constructs 00:06:20
    • Next Steps with R 00:03:22
    • Machine Learning Basics 00:05:41
  2. Chapter 2 : Let's Help Machines Learn
    • Algorithms in Machine Learning 00:04:33
    • Supervised Learning Algorithms 00:16:13
    • Unsupervised Learning Algorithms 00:07:18
  3. Chapter 3 : Predicting Customer Shopping Trends with Market Basket Analysis
    • Market Basket Analysis 00:05:25
    • Evaluating a Product Contingency Matrix 00:07:33
    • Frequent Itemset Generation 00:05:53
    • Association Rule Mining 00:08:35
  4. Chapter 4 : Building a Product Recommendation System
    • Understanding Recommendation Systems 00:06:35
    • Building a Recommender Engine 00:06:25
    • Production Ready Recommender Engines 00:10:15
  5. Chapter 5 : Credit Risk Detection and Prediction – Descriptive Analytics
    • Understanding Credit Risk 00:05:26
    • Data Preprocessing 00:03:32
    • Data Analysis and Transformation 00:02:48
    • Analyzing the Dataset 00:22:05
  6. Chapter 6 : Credit Risk Detection and Prediction – Predictive Analytics
    • Data Preprocessing 00:03:07
    • Feature Selection 00:04:18
    • Modeling Using Logistic Regression 00:07:00
    • Modeling Using Support Vector Machines 00:09:32
    • Modeling Using Decision Trees 00:04:29
    • Modeling Using Random Forests 00:04:13
    • Modeling Using Neural Networks 00:07:15
  7. Chapter 7 : Social Media Analysis – Analyzing Twitter Data
    • Getting Started with Twitter APIs 00:08:27
    • Twitter Data Mining 00:11:12
    • Hierarchical Clustering and Topic Modeling 00:06:52
  8. Chapter 8 : Sentiment Analysis of Twitter Data
    • Understanding Sentiment Analysis 00:04:54
    • Sentiment Analysis Upon Tweets – Polarity Analysis 00:07:18
    • Sentiment Analysis Upon Tweets –Classification-Based Algorithms 00:13:54
  9. Show and hide more

    Oreilly - R Data Analytics Projects


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