Introduction to machine learning

Course Dates: 17/10/19 - 14/11/19
Time: 18:30 - 21:30
Location: KS - Keeley Street
Liv Helen Vage

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.


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.

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 consist 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?

Computers will be provided, but if you prefer to program on your own machine, you must install Python and Jupyter Notebook, in addition to several libraries as instructed in class. Your laptop must have some space for somewhat large data sets. It’s a good idea to bring pen and paper. For the homework tasks, make sure you have access to a computer where you can install Python and Jupyter.

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 link for more information:


Customer Reviews 2 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.

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
We’re sorry. We don’t have a bio ready for the tutor of this class at the moment, but we’re working on it! Watch this space.

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Course Code: CMART01

Please choose a course date 

Thu, eve, 17 Oct - 14 Nov '19

Duration: 5 sessions (over 5 weeks)

Full fee: £249.00
Senior fee: £249.00
Concession: £152.00

Note: Course starts today, check start time

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