Introduction to machine learning
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 come along to this free three-part webinar that has been designed to deepen your understanding of the main concepts present within machine learning. Machine learning combines statistics and computer science to draw inference from patterns in data. It has now become an indispensable skill for data scientists and statisticians alike. These webinars will explore a few of the most important machine learning algorithms and then discuss model selection and evaluation of these models.
More specifically, our first session, on 24 October, 13.00 - 14.00, introduces the main concepts in machine learning. We tackle the big machine learning questions, like:
- what is a ‘model’?
- what is the difference between a supervised model and an unsupervised model?
Our second session, on 26 October, 13.00 - 14.00, moves on to exploring a specific unsupervised method, clustering. We will cover the following types of clustering algorithms:
- centroid-based: specifically, k-means algorithm
- hierarchical-based: divisive (top-down) and agglomerative (bottom-up)
We will then finish, on 1 November, with a live code demonstration in both R/RStudio and Python, using a customer dataset to put the theory into practice. Feel free to join the demonstration in your language of choice. The first half of the code demo, 13.00 - 14.00, will be in Python and the second half, 14.00 - 15.00, will be in R. See the materials under event resources below.
Presenters: Nadia Kennar and Louise Capener
This workshop is suitable for (higher) intermediate users of R and/or Python but there is no need to have experience with machine learning packages. Users should know how to set the working directory in R and/or Python, how to read in data, and how to save scripts and output files.
Depending on your language of choice, Python/Jupyter notebook and/or R and RStudio must be already installed and working.
Level: Session 1 and 2 (beginner), session 3 (Intermediate – requires some basic knowledge of Python and R)
Experience of R and/or Python: Yes
Knowledge of Machine Learning: No
Target audience: Researchers/data scientists/anyone interested in machine learning