What is machine learning? How is machine learning different from classic statistics? What are its applications? What type of models exist within machine learning?
If these are questions that you have then dive into our ML GitHub repo that has been designed to deepen your understanding of the main concepts present within machine learning. Machine learning combines statistics and computer science to mobilise that predictive power. It has now become an indispensable skill for data scientists and statisticians alike. This webinar will explore a few of the most important machine learning algorithms and then discuss model selection and evaluation of these models.
The following topics are covered under this training series:
- Introduction to Machine Learning - covers the concepts behind machine learning, the different types of machine learning models, and the steps involved in undertaking a ML project.
- Unsupervised Methods: Clustering - learn how to approach this common unsupervised machine learning method.
The training materials - including webinar recordings, slides, and sample Python code - can be found in the following folders:
- Python code - run and/or download the Python code using our Jupyter notebook resources.
- R code - run and/or download the R code using our Jupyter notebook resources.