Foundations

Getting started with machine learning

The Foundations Workstream aims to provide a path to gaining competency in common statistical and machine learning techniques by:

• Creating a roadmap of methods to learn
• Gathering together relevant learning materials and,
• Developing notebooks in R and Python with example code, where the methods are applied to reserving data sets.

Learning resources

Some learning resources are listed below.

Working party blog articles

See the bottom of this page for a list of all our Foundations blog posts. The entry point is our introduction post which includes a suggested roadmap to learning machine learning.

Other IFoA curated resources

Name	Format	Content	Level	Language	Review
IFoA Data Science Working Party	Website	Resources curated by the IFoA Data Science Working Party	Mixed	R / Python	Useful complementary resource to our working party
IFoA Data Science Working Party articles	Website	List of articles by the working party on data science topics	Mixed	R / Python	Includes a helpful worked example on XGBoost
IFoA Data Science page	Website	List and links of qualifications in data science, courses, IFoA webinars and other tips on how to get started in data science	Mixed	R / Python	Learning about data science can be overwhelming due to the abundance of resources available. It is helpful to know what the IFoA recommends

Online courses

Name	Format	Content	Level	Language	Review
Stanford Statistical Learning	Course on videos with R examples via website or EDX app	Mostly supervised learning but also touches on unsupervised learning	Beginner	R (but see note below for python)	Very helpful, the course serves as a good reminder on specific statistical techniques while also making the bridge to more advanced techniques used in machine learning
Machine Learning Specialization on Coursera	Online course via Coursera website or app	Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program	Intermediate	Python	It covers foundational ML concepts, such as supervised and unsupervised learning, neural networks, and logistic regression, using real-world examples. The course emphasizes practical applications and uses Python, making it ideal for beginners and intermediates who want hands-on experience
Deep Learning Specialisation on Coursera	Online course via Coursera website or app	Foundations of Deep Learning, case studies, practice of Python and TensorFlow	Intermediate	Python	This is a thorough course. It teaches from first principles what neural networks are and how they work. It does require some familiarity with Python or at minimum with coding
TensorFlow in Practice Specialization on Coursera	Online course via Coursera website or app	Neural Networks with a focus on TensorFlow and its applications	Mixed	R / Python (micro-courses in ML use python)	This is a practical course on Neural Networks. It focuses on applications and less on deep theory
Kaggle	Website	Wide range of content	Mixed	R (but see note below for python)	The section Courses allows the user to focus their learning on particular areas. The section Notebooks gives access to other users' content that include tutorials, reproducable code and data.
Datacamp	Website	Wide range of content with tracks or professional paths being offered. Provides an integrated development environment (IDE) within its platform, so users don’t need to install Python or R locally	Beginner	Python	Popular platform for learning coding and data science skills for free. It offers comprehensive courses on Python, data analysis, machine learning, and many other topics. Their interactive curriculum includes exercises, projects, and certifications, allowing users to learn and practice directly in the browser. It’s suitable for beginners and provides step-by-step guidance for building foundational skills.
FREECODECAMP	Website	Machine Learning with Python Certification with a focus on using tensorflow	Intermediate	Python	Very helpful, the course serves as a good reminder on specific statistical techniques while also making the bridge to more advanced techniques used in machine learning

Notes:

1. Stanford EDX course - Github users have created R and python versions of the course. It is also supported by the textbook An Introduction to Statistical Learning (see below) with videos available here.

2. Kaggle - The notebook on XGBoost and course on Deep Learning may be of particular interest

Textbooks

Name	Format	Content	Level	Language	Review
An Introduction to Statistical Learning	Book (freely available)	Textbook supporting the Stanford Statistical Learning Course	Beginner	N/A	This book is regularly referred to in the Stanford Statistical Learning online course and both follow the same structure
The Elements of Statistical Learning	Book (freely available)	More advanced version of the previous textbook	Intermediate	N/A	Provides more details around the different methods discussed in the introductory text book, An Introduction to Statistical Learning
R for Data Science	Book (freely available)	Introductory guide to data science in R	Beginner	R	Easy guide to common data science tasks in R using the tidyverse
Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems	Book available through O'Reilly subscription or by purchase	Popular introductory book	Beginner	Python	Often referenced as a good entry point into ML
Mathematics for Machine Learning	Book (freely available)	Fundamendal maths tools required to understand ML	Beginner	NA	Recommended by a WP member
Pro Git	Book (freely available)	Definitive guide to understanding git	Mixed	NA	Recommended by a WP member

Posts

As well as the posts listed below, some of the posts in our Research workstream will be of interest, particular those that step through using deep learning for neural networks so we have listed them here along with the rest of the foundations articles.

The older posts are the more entry-level machine learning whereas recent posts consist mainly of the research articles.

Talks