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Courtlyn

Promotion and Events Specialist

Professional Certificate in Machine Learning and Artificial Intelligence

Leverage the power of data to accelerate your career.

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Course Date

STARTS ON

30 August 2022

Course Duration

DURATION

25 weeks, online
15–20 hours per week

Course Fee

PROGRAMME FEE

£3,995 or get £370 off with a referral

Course Information Flexible payment available
Course Information Special group enrolment pricing

Explore financing options available for this programme.
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Apply, automate and achieve your AI and ML career goals

Machine learning (ML) and artificial intelligence (AI) are pushing the limits of possibility and transforming the world around us. Today, to stay ahead of the curve and seize opportunities for innovation, businesses are quickly realising they must anticipate future trends in ML and AI – and to do so, they must engage professionals who can bridge the gap between vision and execution.

The Professional Certificate in Machine Learning and Artificial Intelligence, a joint programme from Imperial College Business School Executive Education and the Imperial College London's Department of Computing was designed with this need in mind. This 25-week online programme provides an immersive, world-class learning experience delivered by leading experts in the field. It will equip you with a unique combination of advanced technical expertise and business acumen that will give you a competitive edge and help you navigate your job search to find exciting career opportunities in ML and AI.

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Programme highlights

The programme covers cutting-edge skills and business strategies in machine learning and artificial intelligence. It will enable you to:

  • Understand foundational and advanced concepts and trends in artificial intelligence, as well as potential real-world implications
  • Determine when machine learning is feasible and can be meaningfully applied to specific business challenges
  • Leverage the power of data and evaluate common machine learning methods to improve predictive performance and refine decision-making strategies
  • Develop and refine machine learning models using Python and industry standard tools to measure and improve performance
  • Identify real-world problems and devise innovative solutions using machine learning models

112,198

The number of jobs posted in the UK between July 2020 and June 2021 requiring applicants to be skilled in ML or AI

Source: EMSI

£60,000

The average salary for a machine learning engineer with three or fewer years of experience

Source: EMSI

6X

The expected growth of the global AI market between 2021 and 2028

Source: Fortune Business Insights 2021
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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:

  • IT and Engineering professionals looking to receive hands-on training in ML and AI and 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 being a part of a cutting-edge 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 mathematics skills
  • Some programming experience

Also recommended:

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

What you will learn

Section 1: Foundations of Machine Learning and Artificial Intelligence
In the first seven modules, you will gain an introduction to the underlying elements of machine learning and its statistical nuances, hone your analytical skills and learn leading-edge techniques for evaluating the performance of a machine learning model.


  • Module 1: Programme Orientation
  • Module 2: Introduction to Machine Learning
  • Module 3: Probability for Machine Learning
  • Module 4: Statistics for Machine Learning
  • Module 5: Generalisation Theory and the Bias-Variance Trade-Off
  • Module 6: Evaluating Predictive Performance
  • Module 7: Advanced Topics in Performance Evaluation

Section 2: Methods for Learning from Data
In these modules, you will explore how decision trees can be leveraged to classify or predict outcomes and solve real-world problems, dive deep into the predictive power of logistic regression and determine which machine learning methods are best suited for solving real-life problems unique to your industry.


  • Module 8: Nearest Neighbour Methods
  • Module 9: Decision Trees, Part I
  • Module 10: Decision Trees, Part II
  • Module 11: Naïve Bayes
  • Module 12: Bayesian Optimisation
  • Module 13: Logistic Regression
  • Module 14: Support Vector Machines
  • Module 15: Unsupervised Learning
  • Module 16: Principal Component Analysis

Section 3: Advanced Topics in Artificial Intelligence and Machine Learning
In the final modules, you will explore deep learning and its relationship to machine learning and artificial intelligence, discover the underlying elements of convolutional neural networks and finally practise the skills required for real-world model optimisations in your capstone project.


  • Module 17: Introduction to Deep Learning
  • Module 18: Neural Networks
  • Module 19: Hyperparameters
  • Module 20: Transparency and Interpretability
  • Module 21: Convolutional Neural Networks
  • Module 22: Biological Basis for Convolutional Neural Networks
  • Module 23: Reinforcement Learning
  • Module 24: Hyperparameter Tuning
  • Module 25: Capstone Competition
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Tools and platforms used in the programme

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Case studies

Case studies and industry examples are drawn from world-class brands and paired with key concepts throughout the programme, providing you the opportunity to examine how machine learning methods can be applied to successfully address business goals.

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Early registrations are encouraged. Seats fill up quickly!

Flexible payment options available. Learn more.

Capstone competition

For your capstone project, you will challenge your knowledge by developing and/or tuning a code base for a custom-designed machine learning competition modeled after the popular Black-Box Optimisation Challenge. You will practise the skills required for real-world model optimisations for a problem of your choice.

The competition takes place during the last 12 weeks of the programme, enabling you to see how tuning your code base improves optimisation on the competition’s leaderboard.

At the conclusion of the programme, you will have built a resume-boosting code base and amassed the skills needed to enter future competitions for prestige, cash prizes and career recognition.

Programme experience

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

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

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A hands-on capstone project to share with potential employers

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

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Associate alumni status from Imperial College London on programme completion

Career preparation and guidance

Breaking into the field of machine learning and artificial intelligence 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 machine learning and artificial intelligence. 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
  • Access to resume referrals to our employer partners
  • 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

Faculty Member PROFESSOR WOLFRAM WIESEMANN

PROFESSOR WOLFRAM WIESEMANN

Professor, Analytics & Operations; Academic Director, MSc Business Analytics; Associate Director, Centre for Process Systems Engineering; Fellow, Computational Optimisation Group; Fellow, Imperial Business Analytics Department of Computing, Imperial College Business School

Wolfram Wiesemann is a professor and group leader of the Computational Optimisation Group in the Department of Computing at Imperial College London. His teaching focuses on linear, discrete and nonlinear optimisation;... More info

Faculty Member PROFESSOR RUTH MISENER

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 and computational software frameworks,... More info

Faculty Member ALEX RIBEIRO-CASTRO

DR ALEX RIBEIRO-CASTRO

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

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 Global Business Analytics MSc.... More info

Faculty Member PROFESSOR CHRISTOPHER TUCCI

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 strategy and innovation management.... More info

Faculty Member PROFESSOR WOLFRAM WIESEMANN

PROFESSOR WOLFRAM WIESEMANN

Professor, Analytics & Operations; Academic Director, MSc Business Analytics; Associate Director, Centre for Process Systems Engineering; Fellow, Computational Optimisation Group; Fellow, Imperial Business Analytics Department of Computing, Imperial College Business School

Wolfram Wiesemann is a Professor and Group Leader of the Computational Optimisation Group in the Department of Computing at Imperial College London. His teaching focuses on linear, discrete and nonlinear optimisation; the theory, algorithms and applications of machine learning; and prescriptive analytics.

Professor Wiesemann holds a joint master’s degree in management and computing from Darmstadt University of Technology and a PhD in operations research from Imperial College London. In addition to his role at Imperial College London, he has been a visiting researcher at several other renowned institutions including Vienna University of Economics and Business, Princeton University, Columbia University and KU Leuven (Belgium).

Professor Wiesemann will serve as faculty lead during weeks covering machine learning concepts and methods.

Faculty Member PROFESSOR RUTH MISENER

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 and computational software frameworks, with current applications including bioprocess optimisation under uncertainty and petrochemical process network design and operations.

Professor Misener earned a BS in chemical engineering from Massachusetts Institute of Technology and a PhD in chemical engineering from Princeton University. She is the recipient of numerous awards and fellowships and is currently an EPSRC Early Career Research Fellow at Imperial College London.

Professor Misener will serve as faculty lead during weeks covering topics related to artificial intelligence.

Faculty Member ALEX RIBEIRO-CASTRO

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 Global Business Analytics MSc. In addition, he has more than 10 years of teaching experience in pure and applied mathematics at institutions across the globe. He also has more than 5 years of consultancy experience—including machine learning and optimisation problems—in industries such as fintech, health, energy and more recently in retail.

Alex Ribeiro-Castro holds an MA and a PhD in mathematics from the University of California (Santa Cruz) and previously held a professorship in mathematics from the Pontifical Catholic University (PUC-Rio).

Alex Ribeiro-Castro will serve as faculty lead during weeks covering mathematical concepts and the use of Python for ML/AI model development.

Faculty Member PROFESSOR CHRISTOPHER TUCCI

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 strategy and innovation management. His primary area of interest is in how companies transition to new business models, technologies and organizational forms. His research focuses on crowdsourcing, Internetworking and digital innovations. Prior to his role at Imperial College London, he was an industrial computer scientist who worked on developing Internet protocols and applying artificial intelligence tools.

Professor Tucci holds a BS in mathematical sciences, a BA in music and an MS in computer science from Stanford University; an MS in technology and policy from Massachusetts Institute of Technology; and a PhD in management from MIT’s Sloan School of Management.

Professor Tucci will serve as faculty lead during weeks covering strategic applications of machine learning methods.

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.

The Imperial learning experience

Human

At Imperial College Business School, we create people-centric learning experiences. From conception through to delivery, we are guided by the principle that learning is a creative, personal and above all, human process. Our high quality, crafted learning environments are highly interactive, community-orientated and actively tutored. Our programmes offer an engaging experience designed to facilitate natural learning behaviours.

Real

No compromises. Our online programmes offer the absolute equivalent of our campus-based programmes. They adopt the same rigorous academic standards, are delivered via our world-leading faculty and offer a comparable high-touch approach to the classroom experience.

Certificate

Example image of certificate that will be awarded after successful completion of this program

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.

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Financing Options Available

UK Residents

  • Knoma – Interest-free financing options available. Contact your Programme Advisor or imperial@emeritus.org to request the application link.

Rest of the World

You can opt for any one of the financing options to cover up to the full cost of the programme tuition. If you are considering financing your programme through one of our partners, the enrolment process can only be completed with the assistance of your programme advisor or by calling +44 148 369 4881.

Please note that loan applications should be submitted no later than four business days prior to the enrolment deadline due to processing time.

Registration for this programme is done through Emeritus. You can contact us at imperial@emeritus.org or schedule a call with an advisor.