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...
Learning modes and locations may be different depending on the course start date. Please check the location of your chosen course and read our guide to learning modes and locations to help you choose the right course for you.
Start Date:21 Jan 2026
End Date:18 Feb 2026
Wed (Evening):18:15 - 21:15
Choose either online or in-person
Location: Hybrid (choose either online or in-person)
Please note: We offer a wide variety of financial support to make courses affordable. Just visit our online Help Centre for more information on a range of topics including fees, online learning and FAQs.
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.
Participants who will attend the full course will receive a City Lit certificate of attendance electronically for their CV or CPD records. The certificate will show your name, course title and dates of the course you have attended.
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?
City Lit offers a variety of progression courses in this subject area. Please click here to view our Programming courses Disclaimer: Use of Third-Party Software This course might require you to either use your own personal account or create an account for the purposes of this course. City Lit cannot accept any responsibility for any failings of the third party or provide technical support. Whilst using the software you will be responsible for abiding by the providers terms and conditions and maintaining your own work.
Thepan Ravindran is a Senior Generative AI engineer at KPMG UK. He holds an MSc in Big Data Science from Queen Mary University of London and completed the Applied Data Science Programme at the Massachusetts Institute of Technology (MIT). During his Master's, he served as a teaching demonstrator for Principles of Machine Learning. Originally from Malaysia and the first in his family to study abroad, he earned a full scholarship for his undergraduate degree before moving to London to further his academic and professional journey. At KPMG UK, Thepan designs and implements AI systems for global clients and trains non-technical professionals to confidently adopt AI. He has also contributed to data-driven projects with the World Health Organisation, Ministry of Health Malaysia and United Nations University. Thepan enjoys the creative problem-solving that programming offers and the impact it can create in society. He is passionate about teaching Python, machine learning, data science, financial modelling, and AI, and is known for helping learners who believe “coding isn’t for people like me” realise that they can do it too.
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.
product
https://www.citylit.ac.uk/introduction-to-machine-learning165709Introduction to machine learninghttps://www.citylit.ac.uk/media/catalog/product/i/n/introduction-to-machine-learning-cmart03.jpg349349GBPInStock/Courses/Courses/Business, marketing & technology/Courses/vm/Skills for Work - Online Employability Courses/Courses/vm/Web design & programming offer/Courses/Business, marketing & technology/Web design and programming/Python/Courses/Business, marketing & technology/Data analytics and statistics/Machine Learning/Courses/Business, marketing & technology/AI & technology/Courses/Business, marketing & technology/AI & technology/Machine Learning22851211179120121339713481135261366712285165312111687168113526One 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. <div data-content-type="html" data-appearance="default" data-element="main">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. <br />
<br />
This is a live online course. You will need:<br />
- Internet connection. The classes work best with Chrome.<br />
- A computer with microphone and camera.<br />
- Earphones/headphones/speakers.<br />
We will contact you with joining instructions before your course starts.</div>74.72585971Introduction to machine learning349349https://www.citylit.ac.uk/media/catalog/product/i/n/introduction-to-machine-learning-cmart03_26.jpgInStockEveningWedHybrid (choose either online or in-person)Available courses5-10 weeksWeekday2026-01-21T00:00:00+00:00Some experienceJan 2026Business, marketing & technologyCMART05349349Introduction to machine learning279227349Thepan Ravindranintroduction-to-machine-learning/cmart05-2526One 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.0000-Available|2026-01-21 00:00:00The 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.<br/><br/>Participants who will attend the full course will receive a City Lit certificate of attendance electronically for their CV or CPD records. The certificate will show your name, course title and dates of the course you have attended.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.Each session treats a different aspect of machine learning. <br/><br/>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.<br/><br/>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. <br/><br/>Session 3: Artificial neural nets - how to determine neural net architecture and hyperparameters. Will briefly cover image processing and convolutional neural networks. <br/><br/>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.• Pre-process your data using some common methods<br/>• Program simple learning algorithms in Python using random forest, boosted decision trees, neural nets, and k-means clustering<br/>• Make informed decisions on choosing variables and tweaking hyperparameters<br/>• Explore machine learning further in an informed manner.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.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.There are no other costs.<p>City Lit offers a variety of progression courses in this subject area. Please click <a href="https://www.citylit.ac.uk/media/wysiwyg/pdf/Web_Programming_Courses.pdf"> here </a> to view our Programming courses<br/>Disclaimer: Use of Third-Party Software<br/>This course might require you to either use your own personal account or create an account for the purposes of this course. City Lit cannot accept any responsibility for any failings of the third party or provide technical support. Whilst using the software you will be responsible for abiding by the providers terms and conditions and maintaining your own work.</p>AI & technologyMachine Learningvirtual2950502Introduction to machine learning349349https://www.citylit.ac.uk/media/catalog/product/i/n/introduction-to-machine-learning-cmart03_27.jpgInStockEveningWedHybrid (choose either online or in-person)Available courses5-10 weeksWeekday2026-04-29T00:00:00+00:00Some experienceApr 2026Business, marketing & technologyCMART04349349Introduction to machine learning279227349introduction-to-machine-learning/cmart04-2526One 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.0000-Available|2026-04-29 00:00:00The 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.<br/><br/>Participants who will attend the full course will receive a City Lit certificate of attendance electronically for their CV or CPD records. The certificate will show your name, course title and dates of the course you have attended.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.Each session treats a different aspect of machine learning. <br/><br/>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.<br/><br/>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. <br/><br/>Session 3: Artificial neural nets - how to determine neural net architecture and hyperparameters. Will briefly cover image processing and convolutional neural networks. <br/><br/>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.• Pre-process your data using some common methods<br/>• Program simple learning algorithms in Python using random forest, boosted decision trees, neural nets, and k-means clustering<br/>• Make informed decisions on choosing variables and tweaking hyperparameters<br/>• Explore machine learning further in an informed manner.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.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.There are no other costs.<p>City Lit offers a variety of progression courses in this subject area. Please click <a href="https://www.citylit.ac.uk/media/wysiwyg/pdf/Web_Programming_Courses.pdf"> here </a> to view our Programming courses<br/>Disclaimer: Use of Third-Party Software<br/>This course might require you to either use your own personal account or create an account for the purposes of this course. City Lit cannot accept any responsibility for any failings of the third party or provide technical support. Whilst using the software you will be responsible for abiding by the providers terms and conditions and maintaining your own work.</p>AI & technologyMachine Learningvirtual279349227CMART05,CMART04NONEWed21/01/26 - 18/02/2618:15 - 21:1518:1521:155 sessions (over 5 weeks)55-10 weeksEveningWeekdayHybridHybrid (choose either online or in-person)Some experienceAvailable courses2026-01-21T00:00:00+00:00,2026-04-29T00:00:00+00:00Jan 2026,Apr 2026Business, marketing & technology349349Introduction to machine learningintroduction-to-machine-learning/cmart05-2526,introduction-to-machine-learning/cmart04-2526One 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.0000-Available|2026-01-21 00:00:00The 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.<br/><br/>Participants who will attend the full course will receive a City Lit certificate of attendance electronically for their CV or CPD records. The certificate will show your name, course title and dates of the course you have attended.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.Each session treats a different aspect of machine learning. <br/><br/>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.<br/><br/>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. <br/><br/>Session 3: Artificial neural nets - how to determine neural net architecture and hyperparameters. Will briefly cover image processing and convolutional neural networks. <br/><br/>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.• Pre-process your data using some common methods<br/>• Program simple learning algorithms in Python using random forest, boosted decision trees, neural nets, and k-means clustering<br/>• Make informed decisions on choosing variables and tweaking hyperparameters<br/>• Explore machine learning further in an informed manner.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.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.There are no other costs.<p>City Lit offers a variety of progression courses in this subject area. Please click <a href="https://www.citylit.ac.uk/media/wysiwyg/pdf/Web_Programming_Courses.pdf"> here </a> to view our Programming courses<br/>Disclaimer: Use of Third-Party Software<br/>This course might require you to either use your own personal account or create an account for the purposes of this course. City Lit cannot accept any responsibility for any failings of the third party or provide technical support. Whilst using the software you will be responsible for abiding by the providers terms and conditions and maintaining your own work.</p>AI & technologyMachine Learningconfigurable
