Imperial College London
Imperial College London - Professional Certificate in Machine Learning and AI
This 25-week online programme provides an immersive, world-class learning experience delivered by leading experts in the field to leverage the power of data to accelerate your career.
Remember it's important you research your options before selecting a course. Late payments may result in fees and could negatively impact your credit file.
What will you
Here's what Emeritus say about their course...
Introduction to Machine Learning
Understanding the approach of machine learning project and describe agents, and basic schematic of a neural network
Probability for Machine Learning
Learn how to calculate absolute, conditional, and total probabilities in Python and Numpy's random number libraries.
Evaluating Predictive Performance
Learn how to apply a confusion matrix to evaluate classification problems with a categorical output variable.
Decision Trees
Learn how a decision tree makes predictions and the difference between entropy and the Gini Index.
What do I get?
What do I need?
Knoma’s Take
Emeritus' approach could be ideal for people with existing careers who want to get ahead. Why? They partner with leading universities around the world and collaborate to make education accessible globally through their online platform. Their courses are focussed on "career acceleration".
CTO Knoma
CAREER CHANGER | 2020 - 2021
Now
Software Developer
Previously
Chef
What does the course cost?
Repay over 11 months or more
Repayment period (months)
Learn for 10 payments of
/ month
Pay 0% interest
Representative example
/ month
Course cost: £1,200
Repayment period: 24 months
Interest rate: 12%
Interest paid: £147.48
This interest rate is personalised to your specific circumstances
You deserve to progress in a career you love. But learning the skills you need can be expensive. We don’t think that’s fair, so we’re doing something about it.
We partner with industry-leading schools and bootcamps so that you can enrol on your chosen course without paying upfront.
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