English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 4h 15m | 1.17 GB
Solve interesting real-world problems using machine learning and R
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.
The course is an enticing journey that starts from the very basics and gradually picks up the pace as it unfolds. Each topic is explained with the help of a project that solves a real-world problem hands-on, thus giving you a deep insight into the world of machine learning.
What You Will Learn
- Utilize the power of R to handle data extraction, manipulation, and exploration techniques
- Use R to visualize data spread across multiple dimensions and extract useful features
- Explore the underlying mathematical and logical concepts that drive machine learning algorithms
- Delve into the world of analytics to correctly predict situations
- Apply reusable code and build complete machine learning systems
- Harness the power of robust and optimized R packages
Table of Contents
Getting Started with R and Machine Learning
1 The Course Overview
2 Delving into the Basics of R
3 Data Structures in R
4 Lists and Data Frames
5 Working with Functions
6 Controlling Code Flow
7 Advanced Constructs
8 Next Steps with R
9 Machine Learning Basics
Let’s Help Machines Learn
10 Algorithms in Machine Learning
11 Supervised Learning Algorithms
12 Unsupervised Learning Algorithms
Predicting Customer Shopping Trends with Market Basket Analysis
13 Market Basket Analysis
14 Evaluating a Product Contingency Matrix
15 Frequent Itemset Generation
16 Association Rule Mining
Building a Product Recommendation System
17 Understanding Recommendation Systems
18 Building a Recommender Engine
19 Production Ready Recommender Engines
Credit Risk Detection and Prediction – Descriptive Analytics
20 Understanding Credit Risk
21 Data Preprocessing
22 Data Analysis and Transformation
23 Analyzing the Dataset
Credit Risk Detection and Prediction – Predictive Analytics
24 Data Preprocessing
25 Feature Selection
26 Modeling Using Logistic Regression
27 Modeling Using Support Vector Machines
28 Modeling Using Decision Trees
29 Modeling Using Random Forests
30 Modeling Using Neural Networks
Social Media Analysis – Analyzing Twitter Data
31 Getting Started with Twitter APIs
32 Twitter Data Mining
33 Hierarchical Clustering and Topic Modeling
Sentiment Analysis of Twitter Data
34 Understanding Sentiment Analysis
35 Sentiment Analysis Upon Tweets – Polarity Analysis
36 Sentiment Analysis Upon Tweets –Classification-Based Algorithms
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