1. Data Mining, Machine Learning, Data Analysis, etc. scikit-learn
Data Science

3. scikit-learn Machine Learning in Python
Simple and efficient tools for data mining and data analysis
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable - BSD license
Language: Python

4. Techniques: Classification Regression Clustering
Identifying to which category an object belongs to.
Regression
Clustering
Dimensionality reduction
Model selection
Preprocessing

5. Examples Face completion with a multi-output estimators
Multilabel classification

6. Multilabel classification

7. Face completion with a multi- output estimators
use of multi-output estimator to complete images
 goal: predict the lower half of a face given its upper half

8. Classification Identifying to which category an object belongs to.
Applications: Spam detection, Image recognition. Algorithms: SVM, nearest neighbors,random forest, ...
Example: Multilabel classification

9. Classification Examples based on real world datasets
Visualizing the stock market structure
unsupervised learning techniques
extract the stock market structure from variations in historical quotes.

10. Classification- Examples http://scikit-learn

11. Regression
Predicting a continuous-valued attribute associated with an object.
Applications: Drug response, Stock prices.
Algorithms:  SVR, ridge regression, Lasso, ...

12. Regression - examples

13. Clustering Automatic grouping of similar objects into sets.
Applications: Customer segmentation, Grouping experiment outcomes
Algorithms:  k-Means, spectral clustering,mean-shift, ...

14. Clustering - Examples

15. Dimensionality reduction
Reducing the number of random variables to consider.
Applications: Visualization, Increased efficiency
Algorithms:  PCA, feature selection, non-negative matrix factorization. .

16. Model selection
Comparing, validating and choosing parameters and models.
Goal: Improved accuracy via parameter tuning
Modules: grid search, cross validation,metrics.

18. Preprocessing Feature extraction and normalization.
Application: Transforming input data such as text for use with machine learning algorithms. Modules: preprocessing, feature extraction.

20. SAS® Enterprise Miner™ https://www. sas
Descriptive and predictive modeling 
Descriptive Modeling: 
uncovers shared similarities or groupings in historical data
Categorizing customers by product preferences or sentiment
Techniques:
Predictive modeling
Classify events in the future or estimate unknown outcomes.
Helps uncover insights for things like customer churn, campaign response or credit defaults.
Example:  using credit scoring to determine an individual's likelihood of repaying a loan

21. SAS - Descriptive Modeling
Clustering
Grouping similar records together.
Anomaly detection
Identifying multidimensional outliers.
Association rule learning
Detecting relationships between records.
Principal component analysis
Detecting relationships between variables.
Affinity grouping
Grouping people with common interests or similar goals (e.g., people who buy X often buy Y and possibly Z).

22. SAS - Predictive Modeling
Classify events in the future or estimate unknown outcomes.
Helps uncover insights for things like customer churn, campaign response or credit defaults.
Example:  using credit scoring to determine an individual's likelihood of repaying a loan

23. SaS - Predictive Modeling techniques
Regression
A measure of the strength of the relationship between one dependent variable and a series of independent variables.
Neural networks
Computer programs that detect patterns, make predictions and learn.
Decision trees
Tree-shaped diagrams in which each branch represents a probable occurrence.
Support vector machines
Supervised learning models with associated learning algorithms.