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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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