CFI 1072 - Machine Learning with Big Data Technology

1. Analytics Framework
2. Exploratory Data Analysis
3. Overview of Machine Learning
4. Market Segmentation
5. Transactional Analysis
6. Anomaly Detection
7. Estimation and Prediction
8. Classification
9. Data Visualization
10. Service Intelligence Advanced Analytics
11. User Behavior Analytics
12. Custom Machine Learning
13. Modeling Use Cases

1. define key classification terms.
2. model data using machine learning.
3. identify analytics project best practices.
4. identify common use cases.
5. define exploratory data analysis.
6. identify exploratory data analysis use cases.
7.  define fundamental concepts and terms associated with machine learning.
8. split data for training and testing models.
9. identify market segmentation use cases.
10. define Transactional Analysis.
11. define anomaly detection.
12. identify anomaly detection use cases.
13. define estimation and prediction.
14. identify estimation and prediction use cases.
15. describe why classification is key to analytics.
16. describe why data visualization is key to analytics.
17. enrich machine data with structured business data.
18. identify deviations across multiple entities-users, devices, and applications-by comparing them against an entity’s baseline and its dynamically generated peer groups. 
19. administer model building, validation, and deployment.
20. build models using the ML Toolkit.
21. apply machine learning analysis to historical data and models to forecast demand, manage inventory, optimize operations, and react to changing conditions.

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