Syllabus

Here you can find the complete syllabus of the course:

Learning Outcomes

Upon successful completion of this course, students will be able to:

Knowledge and Understanding

• Explain what a programming language is and how the Python interpreter works

• Describe different options for running Python code and explain why Python 3 is used

• List Python data types and structural components (functions, classes, modules)

• Explain the meaning and usage of the dot operator (.) in Python

• Describe the purpose of the libraries used for ML with Python (NumPy, Pandas, Matplotlib, Scikit-Learn)

• Describe a range of machine learning techniques and list the differences between supervised and unsupervised learning

• Explain when to use a classification vs a regression algorithm

• Describe and give examples of dimensionality reduction

• List the key features of reinforcement learning and provide an example

• List the six steps of an ML workflow and explain the concepts of training set and test set

Applying Knowledge and Understanding

• Assign values to variables; use indexing and slicing on strings and lists

• Use the built-in function type() to identify data types; write and run code on a Jupyter Notebook

• Write and debug Python code from pseudocode; use data type methods

• Import libraries, call library functions, and use library aliases

• Create, index, modify, and slice lists; create and manipulate nested lists

• Create Python dictionaries and use dictionary methods

• Use indentation, boolean values, and comparison operators in if/elif/else blocks

• Read data from CSV files and URLs into Pandas DataFrames

• Create Series and DataFrames from Python dictionaries; explore and select data from DataFrames

• Loop over sequences, Pandas Series, and DataFrame columns

• Replace non-numerical values with NaN; download datasets from Kaggle

• Implement ML models using the Scikit-Learn Python library

Making Judgements

• Given a dataset, decide whether it is suitable for supervised learning; if so, determine whether classification or regression should be used

• Recognise the most applicable ML method for a given data analysis problem

• Manage errors and exceptions in Python code

• Assess the performance of a ML model

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;