Professional Certificate in Machine Learning and Artificial Intelligence

Develop advanced AI/ML skills to refine decision-making strategies and solve business challenges

Key takeaways

The programme covers cutting-edge skills and organisational strategy in ML and AI. It will enable you to:

Evaluate the feasibility of machine learning solutions for specific business challenges.
Determine the appropriate machine learning methods to improve predictive performance and decision-making strategies.
Analyse the real-world implications of artificial intelligence.
Analyse complex datasets using machine learning to improve organisational performance.
Refine machine learning models using Python to enhance performance metrics.
Evaluate the feasibility of probabilistic and statistical techniques in addressing data.

Testimonials

The programme structure was excellent. The level of learning was pitched well, and a lot of material was covered in a short space of time. The middle third of the programme, which introduced different...

Chris Nightingale,

Management Consultant,

PA Consulting Group

All the modules were great because of the addition of tutorials for real-life applications supported by tools and platforms used in the programme and videos. Office hours and support from both learnin...

Sebastiano Gadolini,

Early-Stage Researcher,

Johnson Matthey

Tools and platforms used in the programme

Case studies and industry examples featured in the programme will help you better understand customer behaviour and the use of analytics in customer segmentation and targeting.

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Who is this programme for?

The Professional Certificate in Machine Learning and Artificial Intelligence requires a background in coding or mathematics. It is particularly relevant for professionals, including:

Early-career IT and engineering professionals who are looking for hands-on training in ML and
AI to upskill themselves in a high-growth field. Job titles may include software engineers and software developers
Data and business analytics professionals who wish to learn about the latest AI tools, techniques and applications to stay ahead of the curve and gain better growth trajectories. Job titles may include business analysts, senior data analysts and data scientists
Recent science, technology, engineering and mathematics (STEM) graduates and academics who are interested in becoming part of a rapidly evolving field with high growth potential and using technology to make a positive impact on the world

Applicants must have:

A bachelor's degree or higher
Strong math skills
Prior programming experience
Written and debugged code in at least one programming language

Also recommended:

An educational background in STEM fields
Technical work experience
Functional experience with Python, R or SQL
Strong experience with statistics and calculus

What you will learn

1Module 0: Programme Orientation and Python Refresher

2Module 1: Maths Concepts in ML/AI

3Module 2: Introduction to ML

4Module 3: Probability for ML

5Module 4: Statistics for ML

6Module 5: Generalisation Theory and the Bias–Variance Trade-Off

7Module 6: Basics of Predictive Performance Evaluation

8Module 7: Advanced Predictive Performance Evaluation

9Module 8: Nearest Neighbour Methods

10Module 9: Decision Trees: Part One

11Module 10: Decision Trees: Part Two

12Module 11: Naïve Bayes

13Module 12: Bayesian Optimisation

14Module 13: Logistic Regression

15Module 14: Support Vector Machines

16Module 15: Neural Networks and Deep Learning: Part One: Introduction

17Module 16: Neural Networks and Deep Learning: Part Two: Advanced Concepts

18Module 17: Neural Networks and Deep Learning: Part Three: Convolutional Neural Networks

19Module 18: Hyperparameters and Hyperparameter Tuning

20Modules 19: Foundations of Generative AI and Large Language Models

21Module 20: Advanced Generative AI and Large Language Models

22Module 21: Unsupervised Learning: Part One – Clustering Techniques

23Module 22: Unsupervised Learning: Part Two – Principal Component Analysis

24Module 23: Reinforcement Learning

25Module 24: Capstone Competition

Case studies

NETFLIX

ZILLOW

DHL

Deutsche Telekom

Alibaba Group

IBM Watson

Programme experience

Job-ready ML/AI skills in a high-demand field

Real-world insights from Imperial College London faculty and industry experts

A hands-on capstone project to share with potential employers

A verified digital certificate of completion from Imperial College Business School Executive Education

Associate alumni status from Imperial College London on programme completion

Career preparation and guidance

Breaking into the field of ML and AI requires a precise combination of technical knowledge and business acumen. Our programme will guide you in developing a career path by assisting you in crafting your elevator pitch and sharpening your interview skills. These services are provided by Emeritus, our learning collaborator for the programme. The support team includes programme leaders and career coaches who will help you reach your learning goals and navigate your job search. The primary goal is to equip you with the skills needed to prepare for a career in ML and AI. However, we do not guarantee a job placement.

Emeritus offers assistance with the following career services:

Live career coaching and open Q&A sessions, resume feedback, mock interviews and career development exercises
A birds's-eye view of the current job market landscape and five-year market trajectory
Assistance in crafting your elevator pitch and preparing for job interviews
Insights on negotiating your salary and opportunity to network with global peers and instructors
Insights into key industry players, latest trends and industry-specific interviews

Programme faculty

PROFESSOR WOLFRAM WIESEMANN

Professor of Analytics and Operations; Head of the Analytics, Marketing and Operations Department; Fellow, Imperial Business Analytics Centre Operations Management Department, Imperial College London

Wolfram Wiesemann is a Professor of Analytics and Operations as well as the head of the Analytics, Marketing and Operations department at Imperial College Business School. In ...

PROFESSOR RUTH MISENER

Professor, Computational Optimisation Department of Computing, Imperial College London

Ruth Misener is a Professor of Computational Optimisation in the Department of Computing at Imperial College London. Her research focuses on numerical optimisation algorithms ...

DR ALEX RIBEIRO-CASTRO

Data Scientist, Senior Teaching Fellow Operations Management Department, Imperial College London

Dr Alex Ribeiro-Castro is a Data Scientist and Senior Teaching Fellow with the Operations Management Department at Imperial College Business School. He also teaches in the Glo...

PROFESSOR CHRISTOPHER TUCCI

Professor, Digital Strategy and Innovation Imperial College Business School

Christopher Tucci is a Professor of Digital Strategy and Innovation at Imperial College Business School. Professor Tucci’s teaching focuses on design thinking, digital strateg...

Become an associate alumni

Take your partnership with Imperial College Business School to the next level by becoming an associate alumnus. Complete the programme to claim your associate alumni status and join our active community.

Certificate

Upon completion of the programme, participants will be awarded a verified digital certificate by Imperial College Business School Executive Education and the Imperial College London Department of Computing.

FAQs

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2The learning experience

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Didn't find what you were looking for? Write to us at learner.success@emeritus.org, or Schedule a call with one of our programme advisers or call us at +44 114 697 4544 (UK) / +1 315 618 2562 (US) / +65 3129 8674 (SG).

Early registrations are encouraged.

Flexible payment options available.

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