digiLab AcademyLearn how to apply AI in the Wild

Getting Started in Machine Learning

Our self-paced online courses, delivered by the team of ML engineers at digiLab, contain everything you need to deploy AI and Machine Learning on real-world problems.

Learn the most in-demand tools and libraries

Python
Numpy
Pandas
TwinLab
Pytorch

We built the digiLab Academy, to enable you to do
"AI in the Wild"

At digiLab, we build next-generation AI/ML solutions to tackle today's biggest and most exciting challenges:

  • Solving nuclear fusion
  • Decommissioning old power-plants
  • Managing airspace more efficiently
  • Designing lighter aeroplanes
  • Reducing the pollution in our rivers

That means working with organisations like the UK Atomic Energy Authority, Jacobs, NATS, Airbus, and South West Water .

Our clients tell us over and over about the skills gap between the candidates they interview, and the employees they want.

So we know what online ML courses really need to deliver

Learn at your own pace where and when suits you

Our online learning platform is designed to help you track and manage your personalised learning journey.

platform

Build a solid foundation for the next step in your career.

Powerful and practical techniques to land a Machine Learning job.

Our curriculum teaches the skills which industry wants & needs.

  • Key Principles of ML to get started.

  • Deep Learning to tackle harder problems.

  • Probabilistic ML for wrangling dodgy data.

  • Functional GPs (Gaussian Processes).

  • Next-Gen UQ (Uncertainty Quantification) for safety-critical systems.

Lecture example 1
Lecture example 1

Knowledge you need to interview with confidence.

In addition to getting you up to speed with cutting edge techniques, your course also includes...

  • Full text documentation and custom notebooks so you can code alongside your course leads.

  • A digiLab Academy Certificate of Completion to add to LinkedIn.

  • A bonus "Machine Learning Careers in Sustainability" guide.

  • Recruitment opportunities from our industry partners, straight to your inbox.

🤖 Use AI to help you learn!

All digiLab Academy subscribers have access to an embedded AI tutor! This is great for...

Helping to clarify concepts and ideas that you don't fully understand after completing a lesson.
Explaining the code and algorithms covered during a lesson in more detail.
Generating additional examples of whatever is covered in a lesson.
Getting immediate feedback and support around the clock...when your course tutor is asleep!

A cutting-edge curriculum delivered by expert educators.

Whether you are a complete beginner or arrive with some knowledge, We'll cover everything you need to know to do "AI in the Wild".

So whenever you're confronted with real-world messy or sparse data, you'll have the knowledge and skills to tackle it.

Theme 1

Key Principles of ML

led by

Professor Tim Dodwell

digiLab.co.uk
Tim will start by focusing on the key ML and data handling skills you'll need to do "AI in the Wild".
  • Machine Learning 101

  • Supervised vs Unsupervised

  • Regression vs Classification

  • ML Workflow

  • Generalised Linear Models

  • Intro to Statistics

  • Exploring Correlations

  • Predictive Power Scores

  • Handling Data

  • Handling Missing Data

  • Handling Outliers

  • Handling Noisy Data

Theme 2

Deep Learning

led by

Dr Andy Corbett

digiLab.co.uk
Next, you'll learn a broad range of methods, including Random Forests and Neural Nets.
  • Deep Learning 101

  • KNN and K-Means

  • Kernel Trick and SVMs

  • Decision Trees

  • Random Forests

  • Gradient Boosting

  • Uncertainty in Forests

  • Neural Net Architectures

  • Loss in Neural Nets

  • RNNs and CNNs

  • Uncertainty in Neural Nets

  • PCA and Autoencoders

Theme 3

Probabilistic ML

led by

Dr Mikkel Lykkegaard

digiLab.co.uk
Finally, you'll tackle UQ, Functional GPs and learn best-practice for presenting results.
  • UQ 101

  • Intro to Bayes

  • Priors and Posteriors

  • Probabilistic Regression

  • Basis Functions / Kernels

  • Likelihoods in GPs

  • Kernel Design

  • Hyperparameter Tuning

  • Descriptive Statistics

  • Summary Plots

  • Visualising Uncertainty

  • Presenting Uncertainty

These themes are developed over our growing course library.

Getting Started in Machine Learning

Getting Started in Machine Learning

Learn key machine learning concepts and build a strong foundation for future learning.

Using General Linear Models for Machine Learning

Using General Linear Models for Machine Learning

Understanding linear models is the key to unlocking the full power of machine learning.

The Kernel Trick: A first look at flexible machine learning

The Kernel Trick: A first look at flexible machine learning

Outsourcing the Guesswork: A Dive into the Kernel Trick and Support Vector Machines.

Tree-based algorithms: Random Forests and Gradient Boosted Trees

Tree-based algorithms: Random Forests and Gradient Boosted Trees

Gold Standard Algorithms: Harnessing the Power of Random Forests and Gradient-Boosted Models.

INTERMEDIATE

An Introduction to Bayesian Uncertainty Quantification

An Introduction to Bayesian Uncertainty Quantification

If you want to deploy AI with confidence, then you need Uncertainty Quantification (UQ).

INTERMEDIATE

AI Ethics: Fairness and Balance in Modern Machine Learning

AI Ethics: Fairness and Balance in Modern Machine Learning

Exploring Boundaries and Unraveling the Ethical Dimensions of AI and Machine Learning.

Deep Learning 101: Getting Started with Neural Networks

Deep Learning 101: Getting Started with Neural Networks

Learn the fundamentals of deep neural networks, including selecting architecture and training algorithms.

Using TensorFlow to Build a Production-Ready Neural Network-Powered Web App

Using TensorFlow to Build a Production-Ready Neural Network-Powered Web App

Build, Train and Deploy a Convolutional Neural Network Image Classifier.

INTERMEDIATE

Strategies and Techniques for Data Cleaning in Excel and Python

Strategies and Techniques for Data Cleaning in Excel and Python

Master essential data cleaning techniques and turn messy data into a valuable resource with effective cleaning techniques in Excel and Python.

Pre-flight checks - Getting Started & Python Basics

Pre-flight checks - Getting Started & Python Basics

Get your coding environment set up and learn the basics of Python.

I know Tim and the digiLab team well, they are leaders in the applied ML space and really understand how to work with and build solutions for industry. They asked me to access the course and give early feedback. I teach a lot of students from our business schools, and many are looking to differentiate themselves by knowing the foundations in machine learning. This course is excellent. Really high quality, lots of interactive examples and well-paced as an introductory course on your way to becoming an AI practitioner. A no-brainer for the self-motivated learner.

Prof Edmond Awad
Edmond Awad
Prof of Computational Ethics
Inventor of the "Moral Machine", ex MIT, Exeter, Max Planck & Oxford.

Ready to start your machine learning journey?

Frequently asked questions