Learning Outcomes of Machine Learning Bootcamps
This bootcamps is about learning how to use tools and technologies to implement Machine Learning to solve reallife problems and about understanding the state of the art of Machine Learning in the industry.
The learning outcomes of these bootcamps have been divided into two levels. For successful completion of the program and to qualify for the completion certificate, you will be required to achieve both levels.
Ability to Illustrate Understanding Outcomes:
The participants after graduating from the bootcamp will be able to illustrate their understanding of:
Level 1:

Current state of Artificial Intelligence in the world today

Machine Learning and its applications in the industry

Supervised vs. Unsupervised Machine Learning

Traditional Machine Learning vs. Deep Learning (Artificial Neural Networks)

Narrow Artificial Intelligence vs. Artificial General Intelligence

General Structure of Machine Learning Methods
Level 2:

Big Data and large databases

KNearest Neighbor Machine Learning

Decision Trees/Random Forest Machine Learning

Linear Regression and Logistic Regression Machine Learning

Deep Learning (Artificial Neural Networks)
Ability to Perform Outcomes:
The participants after graduating from the bootcamp will be able to:
Level 1:

programmatically read large files (with 10 million or more records) and store data in matrices for analysis

use NumPy for handling large matrices (sparse and nonsparse)

show how matrices operation are faster compared to using loops
Level 2:

implement KNearest Neighbor ML using generic Python

implement Linear Regression and Logistic Regression using generic Python

Implement classification and regression Machine Learning

Use Machine Learning Libraries to solve reallife analytics problems