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Machine Learning, Washington D.C.

Emerging Rule Headquarters: Georgetown | Washington D.C. Metro Area

LevelShip™ Machine Learning - Learning Management System (ML-LMS) breakdown:

 

Language: Python

Method: Deep Learning (Convolutional Deep Neural Networks)


Large size of the dataset & the expectance of high performance & fast execution speed for back propagation.

 

Training:

The potential predictable relationships between productivity & career fulfillment within individual skillsets in the training set data mined from governments, schools districts, and/or participating schools (within agreement) & will be cross validated (KFold) in conjunction with participating guardians/parents. Test set will discover
relationships found in training set hold.


The most relevant & 1st step is to mine data sets from Education
Ministries/Departments & participating schools.


Ensemble Learning approach:


1. Virtual personal assistant. (learn more)

2. Behavioral analysis. (learn more)

3. Automatic Transcript. (learn more)

4. Success-rate predictions. (learn more)

5. Behavior surveillance. (learn more)

6. Using Internet of Things (IoT). (learn more)

7. Data recommendation. (learn more)

8. Big Data. (learn more)

ML – LMS Logistic Regression Model Building Steps:


1. Data gathering.
2. Data mining.
3. Classification.
4. Preprocessing Data (Validation).
5. Fit the logistic regression to training set.
6. Predict test set result.
7. Evaluate if logistic regression model learn and understands correlations.

Results: Good model defines the environment in which education deficiencies exists and set up the catalyst for better learning management systems; concisely describe solutions, discover data, methods & performance benefits that work with the model. Furthermore, interprets where the model excels & is more trusted while defining the contrary. Revisit applied education methods & training programs & their effectiveness/efficiency of education systems for individual productivity.