In this course you will learn about a range of machine learning topic areas. You will also learn how to perform the modelling operations using the Python programming language and the package 'scikit-learn'.
Learn more on Introduction to machine learning in PythonThis is an Introduction to Natural Language Processing, and covers the main concepts around text cleaning, processing, exploring datasets, and becoming familiar with working with text based data in Python.
Learn more on Introduction to NLP in PythonThis course is designed to introduce learners to object orientated programming in Python.
Learn more on Introduction to object orientated programming in PythonThis course will give you an understanding of Pyspark, the Python interface to the distributed processing tool Spark.
Learn more on Introduction to PysparkThis course focuses on applying coding skills in Python and building confidence, independence and resilience so that you can continue your learning beyond the classroom.
Learn more on Introduction to PythonThis course focuses on applying coding skills and building confidence, independence and resilience so that you can continue your learning beyond the classroom.
Learn more on Introduction to RThis course introduces the concepts, motivation, and techniques for creating a Reproducible Analytical Pipeline (RAP). It explains the benefits of RAP and gives guidance to help you overcome common problems with it.
Learn more on Introduction to Reproducible Analytical Pipelines (RAP)This course will give an overview of the benefits and main features of RMarkdown.
Learn more on Introduction to RMarkdownThis course has been designed for beginners in the field of machine learning (ML).
Learn more on Machine learning in RIn this course you will learn important concepts relating to modular code and how to convert code to functions. There are modules in either R or Python.
Learn more on Modular Programming in Python and R