Learning Outcomes of Machine Learning Bootcamps

This bootcamps is about learning how to use tools and technologies to implement Machine Learning to solve real-life 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

  • K-Nearest 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 non-sparse)

  • show how matrices operation are faster compared to using loops

 

Level 2:

  • implement K-Nearest 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 real-life analytics problems