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
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