Data Science: Data Mining & Natural Language Processing In R Online Course

Data Science: Data Mining & Natural Language Processing in R

All levels
English

Course Description

MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R: Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED: My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you will easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you will know it all: visualization, stats, machine learning, data mining, and neural networks!

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What you’ll learn

This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools.
Equip you to use R to perform the different exploratory and visualization tasks for data modelling.
Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation.
You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques.
& You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results.

Covering Topics

Section 1 : INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Section 2 : Reading in Data from Different Sources in R

Section 3 : Exploratory Data Analysis and Data Visualization in R

Section 4 : Data Mining for Patterns and Relationships

Section 5 : Machine Learning for Data Science

Section 6 : Unsupervised Classification- R

Section 7 : Dimension Reduction

Section 8 : Supervised Learning Theory

Section 9 : Supervised Learning: Classification

Section 10 : Supervised Learning: Regression

Section 11 : Introduction to Artificial Neural Networks (ANN)

Section 12 : More Web-scraping and Text Data Mining

Section 13 : Gaining Insights from Text Data- Text Mining and Natural Language Processing (NL

Section 14 : Text Data and Machine Learning

Curriculum

  Section 1 : INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
1
Introduction Preview
2
Data and Scripts For the Course
3
Introduction to R and RStudio
4
Start with Rattle
5
Conclusion to Section 1

  Section 2 : Reading in Data from Different Sources in R
6
Read in Data from CSV and Excel Files Preview
7
Read Data from a Database
8
Read Data from JSON
9
Read in Data from Online CSVs
10
Read in Data from Online HTML Tables-Part 1
11
Read in Data from Online HTML Tables-Part 2
12
Read Data from Other Sources
13
Conclusions to Section 2

  Section 3 : Exploratory Data Analysis and Data Visualization in R
14
Remove NAs Preview
15
More Data Cleaning
16
Exploratory Data Analysis(EDA): Basic Visualizations with R
17
More Exploratory Data Analysis with xda
18
Introduction to dplyr for Data Summarizing-Part 1
19
Introduction to dplyr for Data Summarizing-Part 2
20
Data Exploration & Visualization With dplyr & ggplot2
21
Pre-Processing Dates-Part 1
22
Pre-Processing Dates-Part 2
23
Plotting Temporal Data in R
24
Twist in the (Temporal) Data
25
Associations Between Quantitative Variables- Theory
26
Testing for Correlation Preview
27
Evaluate the Relation Between Nominal Variables
28
Cramer's V for Examining the Strength of Association Between Nominal Variable

  Section 4 : Data Mining for Patterns and Relationships
29
What is Data Mining?
30
Association Mining with Apriori
31
Apriori with Real Data
32
Visualize the Rules
33
Association Mining with Eclat Preview
34
Eclat with Real Data

  Section 5 : Machine Learning for Data Science
35
How is Machine Learning Different from Statistical Data Analysis?
36
What is Machine Learning (ML) About? Some Theoretical Pointers

  Section 6 : Unsupervised Classification- R
37
K-means Clustering Preview
38
Fuzzy K-Means Clustering
39
Weighted K-Means Clustering
40
Hierarchical Clustering in R
41
Expectation-Maximization (EM) in R
42
Use Rattle for Unsupervised Clustering
43
Conclusions to Section 6

  Section 7 : Dimension Reduction
44
Dimensionality Reduction-theory
45
PCA
46
Removing Highly Correlated Predictor Variables
47
Variable Selection Using LASSO Regression
48
ariable Selection With FSelector
49
Boruta Analysis for Feature Selection
50
Conclusions to Section 7

  Section 8 : Supervised Learning Theory
51
Some Basic Supervised Learning Concepts
52
Pre-processing for Supervised Learning

  Section 9 : Supervised Learning: Classification
53
What are GLMs?
54
Logistic Regression Models as Binary Classifiers
55
Linear Discriminant Analysis (LDA)
56
Binary Classifier with PCA
57
Our Multi-class Classification Problem
58
Classification Trees
59
More on Classification Tree Visualization
60
Decision Trees
61
Random Forest (RF) classification
62
Examine Individual Variable Importance for Random Forests
63
GBM Classification
64
Support Vector Machines (SVM) for Classification
65
More SVM for Classification
66
Conclusions to Section 9

  Section 10 : Supervised Learning: Regression
67
Ridge Regression in R
68
LASSO Regression in R
69
Generalized Additive Models (GAMs) in R
70
Boosted GAMs
71
MARS Regression
72
CART-Regression Trees in R
73
Random Forest (RF) Regression
74
GBM Regression
75
Compare Models
76
Conclusions to Section 10

  Section 11 : Introduction to Artificial Neural Networks (ANN)
77
What are Artificial Neural Networks?
78
Neural Network for Binary Classifications
79
Neural Network with PCA for Binary Classifications
80
Neural Network for Regression
81
More on Neural Networks- with neuralnet
82
Identify Variable Importance in Neural Networks

  Section 12 : More Web-scraping and Text Data Mining
83
Read in Text Data from an HTML Page
84
Explore Amazon with R
85
More Webscraping With rvest-IMDB Webpage
86
Prior to Mining Data from Twitter
87
Extract Tweets Using R
88
More Twitter Data Extraction Using R
89
Get Data from Facebook Using R
90
Conclusions to Section 12

  Section 13 : Gaining Insights from Text Data- Text Mining and Natural Language Processing (NL
91
Explore Tweet Data
92
Visualize Tweet Sentiment Wordcloud- India's Demonetization Policy
93
More Wordclouds: Amazon Review Data
94
Word Frequency in Text Data
95
Tweet Sentiments- India's Demonetization Policy
96
Tweet Sentiments- Mugabe's Ouster
97
Examine the Polarity of Text
98
Polarity of Individual Tweets
99
Topic Modelling a Document
100
Topic Modelling Multiple Documents
101
Conclusions to Section 13

  Section 14 : Text Data and Machine Learning
102
EDA With Text Data
103
Identify Deceptive Reviews With Supervised Classification
104
Identify Spam Emails with Supervised Classification

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