Syllabus

Course Learning Outcomes

After completing the course, learners will be able to:

Knowledge and understanding

• Explain what is a programming language

• Describe what it is and how it works the Python interpreter

• Describe different options to run Python code

• Explain why we use Python 3

• Explain why there are many programming language

• List Python data types

• Given a piece of code, describe (in comments) line by line what each line of code does

• List structural components of Python language (e.g. functions, classes and modules)

• Explain what is the meaning and usage of “.” (dot) in Python

• Describe the purpose of the libraries needed to do ML with Python

• Describe a range of machine learning techniques

• List the differences between supervised and unsupervised learning

• Explain when using a classification or regression algorithm

• Make examples of dimensionality reduction

• List the reinforcement learning key features

• Make an example of reinforcement learning

• List the 6 ML steps

• Explain what is a training and a test set

Applying knowledge and understanding

• Assign a value to a variable

• Use an index to get a single character from a string

• Use a slice to get a substring

• Use the built-in function type() to identify data types

• Write and run code on a Jupyter Notebook

• Given a piece of pseudo code, write and debug the corresponding code

• Use data type methods

• Import libraries

• Call (use) functions from an imported library

• Import specific items from a library module

• Give a library a short alias when importing it

• Create and index lists 

• Change the values of individual list elements

• Append values to an existing list

• Reorder and slice list elements

• Create and manipulate nested lists

• Create a Python dictionary

• Assign a dictionary item

• Use at least one dictionary method

• Use indentation to create an if block

• Use booleans True and False in if conditions

• Use >, <, >=, <=, === operators

• Read data from a csv file into a Python Data Frame

• Read a csv table from a URL into a pandas Data Frame

• Create a Series and a DataFrame from Python dict

• Explore and select data from Data Frames

• Loop on a sequence of items and print each item

• Loop on a pandas Series

• Loop on a column of a pandas DataFrame

• Replace non numerical values with NaN

• Download a dataset from the Kaggle database

• Implement ML models using the Scikit-Learn Python library

Making judgements

• Given a dataset, decide if it is suitable for supervised learning or not

• If yes, indicate whether classification or regression should be used

• Recognize what machine learning method is most applicable to given data analysis problems 

• Manage errors and exceptions

• Assess the performance of a ML method

Lesson Plan

Course activities

All activities are designed around the course's Learning Outcomes (LOs) — for each LO, the most appropriate activity or combination of activities is selected to help students achieve it.

Depending on the topic and the skills involved, activities may include:

• Short interactive lectures introducing key concepts in programming and machine learning;

• Participatory live coding sessions, where the instructor codes in front of the class and students follow along;

• Hands-on coding sessions (individual, in pairs, or in small groups), where students independently use Python to solve data handling problems and implement ML algorithms;