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lynda Data Science Foundations: Data Mining in Python

Video Introducing this tutorial


Introduction :
Python for data mining
What you should know
Exercise files

1. Preliminaries :
Tools for data mining
The CRISP-DM data mining model
Privacy, copyright, and bias
Validating results

2. Dimensionality Reduction :
Dimensionality reduction overview
Handwritten digits dataset
PCA
LDA
t-SNE
Challenge: PCA
Solution: PCA

3. Clustering :
Clustering overview
Penguin dataset
Hierarchical clustering
K-means
DBSCAN
Challenge: K-means
Solution: K-means

4. Classification :
Classification overview
Spambase dataset
KNN
Naive Bayes
Decision trees
Challenge: KNN
Solution: KNN

5. Association Analysis :
Association analysis overview
Groceries dataset
Apriori
Eclat
FP-Growth
Challenge: Apriori
Solution: Apriori

6. Time-Series Mining :
Time-series mining
Air Passengers dataset
Time-Series decomposition
ARIMA
MLP
Challenge: Decomposition
Solution: Decomposition

7. Text Mining :
Text mining overview
Iliad dataset
Sentiment analysis: Binary classification
Sentiment analysis: Sentiment scoring
Word pairs
Challenge: Sentiment scoring
Solution: Sentiment scoring

Conclusion :
Next steps