LINAS Intro to Python & Machine Learning

Course Overview

• Introduction to the basics of Python and coding a machine learning model, developed for humanities scholars in the Leverhulme Interdisciplinary Network for Algorithmic Solutions (LINAS).

• The course is divided into three units:

• Unit 1: Introduction to Python
• -Ability to understand the structure of a Python program
• -Ability to write a basic Python program
• -To become familiar with the various data types used within a program
• -Develop the ability to code and implement basic logic

• -Develop the skills to debug simple programs and errors

• Unit 2: Data Management
• -Learn how to read and understand data
• -Understand the different stages of a data analysis workload
• -Be able to prepare and clean a dataset for analysis
• -Ability to infer relationships from data

• -Be able to feed a simple dataset to a rudimentary model

• Unit 3: Data Management
• -Understand basics of ML model development
• -Ability to optimise model performance
• -Understand the differences and strengths of supervised/unsupervised learning
• -Familiarity with development and output of DNNs
• -Ability to construct processing pipeline from data input to results

Prerequisites

• None

Textbooks

• None

Schedule

Week	Date	Topic	Materials
1	Mar 18	Inputs, Outputs & Arithmetic Overview of basic Python programming in the Jupyter Notebook environment.	Notebook
2	Mar 25	Data Types & Indexing Basics Introduction to variable types including strings, integers, floats, booleans and lists.	Notebook
3	Apr 1	Conditional Logic, Control Flow and Functions Operating under conditional logic and repeating code with loops and functions.	Notebook
4	Apr 8	Debugging and Basic Error Handling Fixing code, reading errors and troubleshooting.	Notebook
5	Apr 15	Complex Data Types Dictionaries, Classes and Arrays.	Notebook