This course will guide you through the complexities of building and deploying machine learning models to production. We’ll use TensorFlow, an industry-standard library, to build our neural network. We’ll explore the intricacies of dataset compilation, teaching you the ins and outs of the Bing API for creating your own custom datasets. We’ll also discuss how to reduce the footprint of your ML models using ONNX, as well as saving and retrieving them from a data store using the S3 buckets API. When you complete this course, you’ll have the tools to develop your own cloud-based, machine learning-powered web applications.

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

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

If you've ever used Google Maps or Google Earth, you know that mapping applications are really useful in lots of different situations. They allow us to navigate, find somewhere to eat, and see the Earth changing over time. Working with spatial data allows researchers and data scientists to look even deeper, investigating and analysing how space and place affects the world. Building geospatial web apps, which allow users to interact with and explore models and data are a powerful way of communicating results and analysis, both within organisations and to the public. 

Allow users to interact with and explore models and data directly in their browser

Building a Geospatial Web App in Python

In this course, we venture into the realm of AI Ethics, examining and attempting to unpack the societal implications surrounding data and privacy in our increasingly digital lives. We’ll probe the intriguing intersection of AI and creativity: can machine learning models genuinely exhibit creativity or are we attributing human characteristics to algorithms? Hear firsthand from artists about the influence of generative AI on their work. With a blend of theoretical discussions and practical insights, this course provides food for thought on the ethical dimensions of modern machine learning.

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

AI Ethics: Fairness and Balance in Modern Machine Learning

Linear models might not seem that exciting, but they provide the foundation you need to understand advanced topics like Deep Neural Networks or Gaussian Processes further down the line. In this course, we’ll work through key concepts and techniques, including Predictive Power Score (or PPS) and Principle Component Analysis (PCA). We'll also cover the important topic of over-fitting and how we address this using regularisation methods.

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

Using General Linear Models for Machine Learning

With a parametric model, we guess the shape of the function and fit with suitable parameters. With a non-parametric model, we outsource the guesswork to the model itself: the machine does the thinking! As a first foray into non-parametric algorithms, we look at the kernel trick, which amplifies our input data by combining all the features under the hood. The first model we’ll examine is Support Vector Machines for classification and regression tasks.

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

The Kernel Trick: A first look at flexible machine learning

The archetypal and top-performing machine learning algorithms are based on the humble decision tree. Trees are explainable, intuitive algorithms which are cheap to train and run. But on their own they need support. In this course, we’ll investigate the fundamentals of Decision Trees and then explore their extension to both Random Forests and Gradient Boosted Trees (inc. ‘XGBoost’). These two algorithms are the gold medalists of ML models on any leaderboard, an essential tool for your collection.

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

Tree-based algorithms: Random Forests and Gradient Boosted Trees

Are you ready to start your machine learning (ML) journey? This course will give you an accessible entry point into this revolutionary field. When you complete this course, you’ll understand core concepts in machine learning and, fundamentally, what are machine learning algorithms trying to do. With a focus on 'math light' explainers, hands-on exercises and real-world examples, our self-paced approach will give you a solid foundation in the core concepts of modern ML.

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

Getting Started in Machine Learning

The foundation of every successful machine learning model is clean, high-quality data. But what does clean data look like, and how can you ensure the data you're working with meets that standard? Whether you're dealing with tabular data in Excel or tackling larger datasets in Python, understanding effective strategies for data cleaning is crucial. In this course, we’ll equip you with the essential techniques to turn messy, incomplete, and inconsistent data into a valuable resource.

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

Strategies and Techniques for Data Cleaning in Excel and Python

The aim of this course is not to teach you data science or machine learning but rather to lay the groundwork allowing you to work efficiently through the following courses with the rest of the digiLab team. We’ll start with the basics and cover getting Python installed and your coding environment set up. Then, we take a whistle-stop tour through Python fundamentals to familiarise you with basic programming concepts like loops, branching logic, functions and even some object-oriented programming.

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

Getting Started & Python Basics

Deep neural networks are now solving problems that, until recently, were out of reach. You’ve no doubt seen them pop up in the news as part of the AI revolution. But let’s take a step back. What are neural networks? What is a neuron? What makes them deep? And how can we train and use these models? In this course, we start at the beginning, explaining the fundamental concepts and introducing programming essentials to prepare you to deploy and understand deep neural networks.

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

Deep Learning 101: Getting Started with Neural Networks

If you want to deploy AI in the Wild with confidence, then you need Uncertainty Quantification (UQ). Fundamentally, we must be able to assess the reliability of our estimations. This course will introduce you to the many faces of Bayesian Inference - the gold standard of robust and explainable UQ - from the theoretical foundations to Bayesian parameter estimation.

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

An Introduction to Bayesian Uncertainty Quantification

Machine Learning for Fusion Part 2

Predicting Performance for Magnetic Fusion

Predict in twinlab

Predict in twinLab

Gaussian Processes | Part 3

autoML in twinLab

Part 1 of Machine Learning for Fusion Series

Introduction to Fusion

If you're building a model or set of experiments from scratch, sometimes you need a particular scheme of sampling to effectively span the entire design space at hand. You can do this with a simple function call in twinLab.

twinLab Feature Release: Initial Design Spaces

Gaussian Processes | Part 2

The Kernel Cookbook

twinLab now provides the functionality to perform dimensionality reduction on data, to truncate the number of dimensions and train GPs effectively.

twinLab Feature Release: Dimensionality Reduction for Functional Data

Understanding Gaussian Processes - Part 1

What are Gaussian Processes?

Part 3 - Implement Retrieval-Augmented Generation (RAG) to ground our AI Data Assistant with custom data

Completing our AI Data Assistant with Custom Knowledge using a Pinecone Vector Database

Part 2 - Using prompt templates, chains and tools to supercharge our assistant's capabilities

Expanding our AI Data Assistant to use Prompt Templates and Chains

Part 1 - Building a Streamlit App for Exploratory Data Analysis

Build an AI Data Assistant with Streamlit, LangChain and OpenAI

How digiLab solves them

Inverse Problems in Science and Engineering

How To Place Sensors in a Drainage Network with Confidence.

senSiteUQ: A New Paradigm for Sewer Network Monitoring

Using Machine Learning to Predict EPC Scores

autoEPC: A Step Change for Energy Efficiency

Exploring the likely uses and limitations of Python in Excel

Python In Excel, What Impact Will It Have?

One-Hot Encoders and First Order Sequence Models

Understanding Transformers : One hot Encoders and First Order Sequence Models

An advanced new feature for our twinLab Software

Introducing Active Learning with twinLab

Large Scale Uncertainty Quantification: UM-Bridge makes it easy!

Large Scale Uncertainty Quantification

Comparing the performance of models for binary classification tasks

Receiver Operating Characteristic Curve...the What, Why and How

An introductory explainer to the key differences between supervised and unsupervised learning

Supervised vs unsupervised learning...and more

Use twinLab to build an emulator for the distribution of structure in the universe

twinLab Case Study: Emulating Cosmological Structure with twinLab

Tackling false negatives - a method to help rebalance imbalanced datasets

Rebalancing your data with the Synthetic Minority Oversampling Technique (aka SMOTE)

Build AI agents with an LLM brain and access to the internet - ready to do your bidding!

LangChain - A Toolbox for Supercharging Large Language Models

Essential bedfellows in the age of AI

Ethics and Machine Learning

An exploration of the Curse of Dimensionality and how it influences data collection and model selection

What is the Curse of Dimensionality in Machine Learning?

Using AI to evaluate the solar potential of modern cities

How to Build a Digital Twin of a City

How we used Midjourney v5 to generate unique AI in the Wild course images that pop!

Generative AI is Changing the World

Making tech fairer, making fairer tech

Promoting Equality, Diversity, and Inclusion in AI and ML

Some familiar and some less familiar ML experts and influencers to expand your mind

Which ML Experts should you Follow in 2023?

We didn't start the fire, but AI/ML can put it out

Learning AI/ML to make a positive impact on sustainability: 11 sectors

Dealing with Missing and Nonsense Data

Data Cleaning: What, Why, How?

What's the difference between Machine Learning and Data Science?

Machine Learning vs Data Science Careers

Data thinning, core subset selection, and data compression

DaFT: DerivAtive-Free Thinning

We examine the tools and skills which companies will want to see on your CV/resume

Get a Machine Learning Job - Tools and Skills You Need to Know

Let's take a look at some common interview questions for machine learning jobs

Get a Machine Learning Job - 8 Interview Questions We Ask!

How do you land your first job in Machine Learning?

Get a Machine Learning Job - How to get a data science internship

6 Machine Learning books I would suggest any of our machine learning engineers at digiLab

Get a Machine Learning Job in 2023 - Six Top Machine Learning Books

An introduction to dense, convolutional and recurrent neural networks

Neural Networks: Three Key Types