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R Data Analytics Projects

Video Description

Solve interesting real-world problems using machine learning and R

About This Video

  • Learn to build your own machine learning system with this example-based practical guide
  • Get to grips with machine learning concepts through exciting real-world examples
  • Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning

In Detail

With 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

Table of Contents

  1. Chapter 1 : Getting Started with R and Machine Learning
    1. The Course Overview 00:03:26
    2. Delving into the Basics of R 00:06:11
    3. Data Structures in R 00:09:23
    4. Lists and Data Frames 00:08:38
    5. Working with Functions 00:04:15
    6. Controlling Code Flow 00:03:30
    7. Advanced Constructs 00:06:20
    8. Next Steps with R 00:03:22
    9. Machine Learning Basics 00:05:41
  2. Chapter 2 : Let's Help Machines Learn
    1. Algorithms in Machine Learning 00:04:33
    2. Supervised Learning Algorithms 00:16:13
    3. Unsupervised Learning Algorithms 00:07:18
  3. Chapter 3 : Predicting Customer Shopping Trends with Market Basket Analysis
    1. Market Basket Analysis 00:05:25
    2. Evaluating a Product Contingency Matrix 00:07:33
    3. Frequent Itemset Generation 00:05:53
    4. Association Rule Mining 00:08:35
  4. Chapter 4 : Building a Product Recommendation System
    1. Understanding Recommendation Systems 00:06:35
    2. Building a Recommender Engine 00:06:25
    3. Production Ready Recommender Engines 00:10:15
  5. Chapter 5 : Credit Risk Detection and Prediction – Descriptive Analytics
    1. Understanding Credit Risk 00:05:26
    2. Data Preprocessing 00:03:32
    3. Data Analysis and Transformation 00:02:48
    4. Analyzing the Dataset 00:22:05
  6. Chapter 6 : Credit Risk Detection and Prediction – Predictive Analytics
    1. Data Preprocessing 00:03:07
    2. Feature Selection 00:04:18
    3. Modeling Using Logistic Regression 00:07:00
    4. Modeling Using Support Vector Machines 00:09:32
    5. Modeling Using Decision Trees 00:04:29
    6. Modeling Using Random Forests 00:04:13
    7. Modeling Using Neural Networks 00:07:15
  7. Chapter 7 : Social Media Analysis – Analyzing Twitter Data
    1. Getting Started with Twitter APIs 00:08:27
    2. Twitter Data Mining 00:11:12
    3. Hierarchical Clustering and Topic Modeling 00:06:52
  8. Chapter 8 : Sentiment Analysis of Twitter Data
    1. Understanding Sentiment Analysis 00:04:54
    2. Sentiment Analysis Upon Tweets – Polarity Analysis 00:07:18
    3. Sentiment Analysis Upon Tweets –Classification-Based Algorithms 00:13:54