English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3h 51m | 783 MB
Data science continues to grow in sophistication and demand at an exponential rate. Data mining is the area of data science that focuses on finding actionable patterns in large and diverse datasets: clusters of similar customers, trends over time that can only be spotted after disentangling seasonal and random effects, and new methods for predicting important outcomes. Instructor Barton Poulson focuses on data mining in R, presents a broad range of algorithms including machine learning methods, and offers important information on laws and policies that affect data mining. Barton gives an overview of dimensionality reduction. He introduces clustering, including hierarchical clustering, then goes into association analysis. He explains time-series mining and decomposition, then concludes with text mining, sentiment analysis, and sentiment scoring.
Table of Contents
1 R for data mining
2 Who should watch this course
3 Exercise files
4 Tools for data mining
5 The CRISP-DM data mining model
6 Privacy, copyright, and bias
7 Validating results
8 Dimensionality reduction overview
9 Dataset Handwritten digits
10 PCA
11 LDA
12 t-SNE
13 Challenge PCA
14 Solution PCA
15 Clustering overview
16 Dataset Penguins
17 Hierarchical clustering
18 K-means
19 DBSCAN
20 Challenge K-means
21 Solution K-means
22 Classification overview
23 Dataset Spambase
24 K-nn
25 Naive Bayes
26 Decision trees
27 Challenge K-nn
28 Solution K-nn
29 Association analysis overview
30 Dataset Groceries
31 Apriori
32 Eclat
33 CBA
34 Challenge Apriori
35 Solution Apriori
36 Time-series mining overview
37 Dataset AirPassengers
38 Time-series decomposition
39 ARIMA
40 MLP
41 Challenge Decomposition
42 Solution Decomposition
43 Text mining overview
44 Dataset The Iliad
45 Sentiment analysis Binary classification
46 Sentiment analysis Sentiment scoring
47 Visualizing Word pairs
48 Challenge Sentiment scoring
49 Solution Sentiment scoring
50 Next steps
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