AI Product Manager

Module 1: Leveraging Data with AI

Gain a solid understanding of AI and machine learning fundamentals, including key unsupervised and supervised models. Learn to create business cases for AI applications and optimise AI product use based on data availability and business metrics.

Lesson 1: Using AI and Machine Learning in Business

• Understand the impact of AI and machine learning on business strategies.
• Explore examples of traditional and generative AI across various industries.
• Learn how to identify potential AI applications within your organisation and create compelling AI solution proposals.
• Learn how to maximise the use of AI to drive business results.
• Develop skills to communicate effectively about AI projects with stakeholders.

Lesson 2: AI Personalisation and Unsupervised Machine Learning

• Learn how AI personalisation tailors experiences, services and recommendations to individual users.
• Explore different types of recommendation systems and how to evaluate their performance.
• Explore common algorithms used in clustering and their applications in AI personalisation.
• Learn how to integrate AI personalisation into your product strategy and align it with business goals.

Lesson 3: Predicting the Future with AI

• Understand real-world applications of AI in forecasting, classification and real-time optimisation.
• See how to apply linear regression, trend analysis and autoregression.
• Understand the ethical and societal impacts of AI, including bias and transparency.

Course project: Product Requirements Document (PRD) for an AI-Enabled Gift Recommendation System MVP

In this project, you will create a product requirements document to define the Minimum Viable Product (MVP) for an AI-Enabled Gift Recommendation System.

• Define specific and measurable metrics that will help evaluate success for the MVP.
• Select appropriate AI models and identify the necessary data sources to power the recommendation system.
• Create user stories that clearly articulate features, benefits and the desired outcomes for users.
• Identify metrics useful for influencing future product improvements.
• Identify the most appropriate AI model for the project and the data necessary to power it.
• Describe what to build in crisp User Stories.

Module 2: Bespoke Datasets for Multimodal AI Products

Understand the importance of bespoke datasets for AI accuracy and domain-specific applications. Learn how to create and curate high-quality multimodal datasets (text, video and audio) for AI products, combining supervised machine learning with computer vision techniques. Gain insights into the key stakeholders in AI product development and finish with a project to develop a roadmap for an AI-powered video creation app.

Lesson 1: Supervised Machine Learning and Computer Vision

• Learn the fundamentals of supervised machine learning, including data requirements and performance evaluation.
• Explore the principles of computer vision, its historical development and practical applications.
• Create a product proposal that integrates supervised machine learning and computer vision to address a real-world problem.
• Understand how to apply AI technologies in various products, from simple applications to complex systems like autonomous vehicles.

Course Project: Tasty Cuts: An AI-Powered Video Creation App for Home Chefs

In this project, you will define the roadmap for an AI-powered mobile app for home chefs. You’ll break down barriers towards widespread adoption by considering the product features and prioritisation to make it easier for home chefs to create short-form cooking videos.

• Define a product goal for a medical diagnostic tool.
• Design an annotation job for a medical image dataset.
• Consider metrics for success, how one might improve the annotation design and design test questions for annotators.

Module 3: Generative AI Products

Learn the fundamentals of AI models, including how neural networks make decisions and the training process. Understand how training data impacts model performance, evaluate model results and explore techniques like transfer learning and neural architecture search to make AI more accessible to diverse users.

Lesson 1: NLP and Conversational AI

• Learn how a neural network learns from training data.
• Test data will be used to evaluate a trained model according to metrics like accuracy, precision and recall.
• Learn how to use pre-trained models to transfer learning from one resource to another.

Course Project: Innovative Solutions with Large Language Models (LLMs)

In this project, get experience building models using automated ML, from data to results (no coding required). Build your own model using Google AutoML for a medical imaging use case. Then, the model will be implemented with four different variants of the data to appreciate how the data affects the performance of the model.

• Build and train a model using Google’s AutoML.
• Evaluate several models and decide on the best model for a given product use case.