Introduction to machine learning

Course Dates: 21/09/20 - 21/10/20
Time: 18:30 - 20:00
Location: Online

One of the great advances in technology is that machines can learn without humans teaching them explicit rules – e.g. letting machines train on samples of speech allows Siri to recognise your commands. Machine learning is a large part of artificial intelligence, and a mystery to most of us. This practical course teaches you how to program learning algorithms in Python. We will cover fundamentals of classification, natural language processing, financial predictions and much more. You will learn elements of data mining, how to choose a learning algorithm, and how to tweak parameters of the algorithm. We will briefly cover the theory behind the algorithms, so some maths knowledge is useful, but not required. To enrol, you must have experience with Python or a similar programming language, e.g. have taken City Lit’s Introduction to Python or Introduction to R programming course.

This course will be delivered online. For more information please see our guide to online learning.


What is the course about?

The course serves as a practical guide on how to program machine learning. It will cover some of the most common learning methods. Explanations of how the learning algorithms work will be provided, but the main emphasis is on how they are implemented in practice. The course will briefly cover learning algorithms for images, but most of the course will consider data frames containing words and numbers. This course has an ambitious curriculum – you should be prepared for a somewhat high-paced course, but ample support will be given along the way.

This is a live online course. For more information please see our guide to online learning.
We will contact you with joining instructions before your course starts.

What will we cover?

Each session treats a different aspect of machine learning.

Session 1: Introduction to machine learning - quick history, how it is used today, and data pre-processing.Handling missing values, encoding variables etc. We will spend some time getting familiar with data frames in Python. Elements of natural language processing.

Session 2: Common regression and classification methods with emphasis on random forest and boosted decision trees. Basics of learning algorithms. How loss functions work and optimisation of these. Choices of hyperparameters.

Session 3: Artificial neural nets - how to determine neural net architecture and hyperparameters. Will briefly cover image processing and convolutional neural networks.

Session 4: Unsupervised machine learning and summary, including k-means clustering. Explanation of some unsupervised learning methods with an emphasis on k-means clustering. Summary of common machine learning methods and outline of when to use what. Guidance on how to explore machine learning further.

What will I achieve?
By the end of this course you should be able to...

• Pre-process your data using some common methods
• Program simple learning algorithms in Python using random forest, boosted decision trees, neural nets, and k-means clustering
• Make informed decisions on choosing variables and tweaking hyperparameters
• Explore machine learning further in an informed manner.

What level is the course and do I need any particular skills?

This course is aimed at people with some programming experience, but no machine learning experience. You must have programmed in Python, R, or a similar language before. You should be comfortable with e.g. for-loops and if-statements. You must be comfortable using a computer. Some mathematical understanding is favourable, but not necessary.

How will I be taught, and will there be any work outside the class?

The session consists of a combination of presentations and coding. This is a practical course with an emphasis on implementing what you learn. There will therefore be homework after each week where you explore what you learnt in class in more depth. There will be room for you to explore your topics of interest in several of these. A larger project will be assigned after the third week. If you cannot complete any homework, you are still welcome to join the course, but be aware that you may lose out on some of learning outcomes.

Are there any other costs? Is there anything I need to bring?

There are no other costs.

When I've finished, what course can I do next?

You may be interested in data analytics with Python: introduction or attending one of our maths for programming such as: Linear algebra and optimisation for machine learning, Algorithms in Python. You may also be interested in more programming courses, please visit our website for more information.


Customer Reviews 4 item(s)

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Reviews below are by students who have attended this course, regardless of the course teacher. Please be aware you may not be booking onto a course with the same teacher.

Do it, absolutely fascinating. A great insight into this highly topical and fundamental plank of AI. Very well taught with excellent examples and collateral
Course Rating
Review by Dave - 51st year in IT! / (Posted on 31/10/2019)
Excellent course covering a wide range of machine learning aspects taught by lectures and demonstrations with a good mix of theory, practical coding and projects. Helpful suggestions for resources and opportunities for students to focus on areas of particular interest.
Course Rating
Review by Anonymous / (Posted on 17/10/2019)
This is an intense, well structured introduction to Machine Learning with Python. To get the most out of the course I suggest moderate experience with Python and programming and some knowledge of Machine Learning algorithms.
Course Rating
Review by Ant / (Posted on 15/10/2019)
Great course
Course Rating
Review by Anonymous / (Posted on 05/03/2019)
Tutor Biographies
Liv Helen Vage

Liv is a physicist with background in machine learning. She completed her masters degree at University College London, with a thesis in theoretical astrophysics, and is currently working on a PhD in particle physics at Imperial College. She first started exploring machine learning at the Wolfram Summer School in Boston, and has since done internships with BT in the UK and DESY in Germany. Her PhD centrers around using novel methods for high performance computing and machine learning for the upgrade of the compact muon solenoid experiment at CERN in Switzerland.

Please note: We reserve the right to change our tutors from those advertised. This happens rarely, but if it does, we are unable to refund fees due to this. Our tutors may have different teaching styles; however we guarantee a consistent quality of teaching in all our courses.

Book your place

Course Code: CMART04

Mon+Wed, eve, 21 Sep - 21 Oct '20

Duration: 10 sessions (over 5 weeks)

Full fee: £279.00
Senior fee: £279.00
Concession: £170.00

Or call to enrol: 020 7831 7831

Any questions?
or call 020 7492 2515

Please note: we offer a wide variety of financial support to make courses affordable. For more information visit our online Help Center. You can also visit the Information, Advice and Guidance drop-in service, open from 12 – 6.45, Monday to Friday.