Study Guide

Objectives

The student understands basic concepts of AI and Machine Learning. The student can create and use Machine Learning Algorithms in Python. The student learns how to make analysis and predictions and knows which Machine Learning model to choose for each type of a problem.

Content

- Basic concepts of AI and Machine Learning
- Unsupervised and Supervised learning
- Regression, Classification
- Naïve Bayes, Decision Trees and Neural Network Algorithms
- Training and validation of models
- Options for taking models into production

Assessment criteria, satisfactory (1-2)

The student knows about the basic concepts of AI and Machine Learning. The student can apply at least some unsupervised or supervised learning applications. The student can use a regression or a classification algorithm with support. The student can create an application using either Naïve Bayes, Decision Trees or Neural Network Algorithms. The student can setup training and validation processes for the trained models. The student can setup production testing for the trained models.

Assessment criteria, good (3-4)

The student knows and understands the basic concepts of AI and Machine Learning. The student can apply both unsupervised and supervised learning for applications. The student can create applications with some regression and classification algorithms. The student can create working applications using Naïve Bayes, Decision Trees or Neural Network Algorithms. The student can setup and apply training and use validation methods for the trained models. The student can follow the common procedures of production testing for the trained models.

Assessment criteria, excellent (5)

The student knows and understands in depth the basic concepts of AI and Machine Learning. The student can apply both unsupervised and supervised learning for various applications. The student can use regression and classification algorithms where appropriate. The student can create versatile applications using Naïve Bayes, Decision Trees and Neural Network Algorithms. The student can implement various training and validation solutions for the trained models. Student can execute reliable production testing for the trained models.