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Machine Learning for All

Instructor: Prof Marco Gillies

193,645 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

3,536 reviews

Beginner level

No prior experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

3,536 reviews

Beginner level

No prior experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

What you'll learn

You will understand the basic of how modern machine learning technologies work
You will be able to explain and predict how data affects the results of machine learning
You will be able to use a non-programming based platform train a machine learning module using a dataset
You will be able to form an informed opinion on the benefits and dangers of machine learning to society

Skills you'll gain

Tools you'll learn

Classification Algorithms

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments¹

AI Graded see disclaimer

Taught in English

91% of learners achieved a positive career outcome

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There are 4 modules in this course

Machine Learning, often called Artificial Intelligence or AI, is one of the most exciting areas of technology at the moment. We see daily news stories that herald new breakthroughs in facial recognition technology, self driving cars or computers that can have a conversation just like a real person. Machine Learning technology is set to revolutionise almost any area of human life and work, and so will affect all our lives, and so you are likely to want to find out more about it. Machine Learning has a reputation for being one of the most complex areas of computer science, requiring advanced mathematics and engineering skills to understand it. While it is true that working as a Machine Learning engineer does involve a lot of mathematics and programming, we believe that anyone can understand the basic concepts of Machine Learning, and given the importance of this technology, everyone should. The big AI breakthroughs sound like science fiction, but they come down to a simple idea: the use of data to train statistical algorithms. In this course you will learn to understand the basic idea of machine learning, even if you don't have any background in math or programming. Not only that, you will get hands on and use user friendly tools developed at Goldsmiths, University of London to actually do a machine learning project: training a computer to recognise images. This course is for a lot of different people. It could be a good first step into a technical career in Machine Learning, after all it is always better to start with the high level concepts before the technical details, but it is also great if your role is non-technical. You might be a manager or other non-technical role in a company that is considering using Machine Learning. You really need to understand this technology, and this course is a great place to get that understanding. Or you might just be following the news reports about AI and interested in finding out more about the hottest new technology of the moment. Whoever you are, we are looking forward to guiding you through you first machine learning project.

Module details

In this week you will learn about artificial intelligence and machine learning techniques. You will learn about the problems that these techniques address and will have practical experience of training a learning model.

What's included

6 videos4 readings3 assignments4 discussion prompts1 plugin

6 videosTotal 27 minutes

Introduction: Computers that see2 minutes
Artificial intelligence5 minutes
Machine learning6 minutes
Machine learning algorithms6 minutes
Interview with Machine learning experts8 minutes
Week 1 summary1 minute

4 readingsTotal 35 minutes

Course structure and navigation5 minutes
Learn effectively on this course5 minutes
Welcome to Machine Learning for All10 minutes
Machine learning exercise15 minutes

3 assignmentsTotal 80 minutes

Computers that see20 minutes
Practice quiz – Alpha Go30 minutes
Machine learning summative quiz30 minutes

4 discussion promptsTotal 180 minutes

What is AI?60 minutes
What makes AI hard?30 minutes
How did you find machine learning?30 minutes
A machine learning application60 minutes

1 pluginTotal 15 minutes

Machine learning15 minutes

This week you will learn about how data representation affects machine learning and how these representations, called features, can make learning easier.

What's included

7 videos2 assignments3 discussion prompts

7 videosTotal 36 minutes

The bit3 minutes
Bytes and numbers5 minutes
Other types of data6 minutes
Introduction to Data Features1 minute
Data features4 minutes
Neural networks6 minutes
Interview: Data Features11 minutes

2 assignmentsTotal 90 minutes

Practice quiz – Bag of words30 minutes
Data features summative quiz60 minutes

3 discussion promptsTotal 50 minutes

Data representation in bits10 minutes
Data features10 minutes
What have you learned?30 minutes

In this topic you will get ready to do your own machine learning project. You will learn how to test a machine learning project to make sure it works as you want it to. You will also think about some of the opportunities and dangers of machine learning technology.

What's included

6 videos3 readings1 assignment4 discussion prompts1 plugin

6 videosTotal 37 minutes

Introduction to Machine Learning in practice1 minute
Testing3 minutes
Problems with machine learning4 minutes
Applications of machine learning3 minutes
Dangers of machine learning8 minutes
Interview: Benefits and dangers of machine learning19 minutes

3 readingsTotal 40 minutes

Trying different datasets15 minutes
Evaluating the four datasets15 minutes
Further Reading10 minutes

1 assignmentTotal 60 minutes

Machine learning in practice summative quiz60 minutes

4 discussion promptsTotal 100 minutes

How does machine learning go wrong?30 minutes
Applications of machine learning30 minutes
Dangers of machine learning30 minutes
What have you learned?10 minutes

1 pluginTotal 120 minutes

Trying different datasets120 minutes

In this final topic you will do your own machine learning project: collecting a dataset, training a model and testing it.

What's included

4 videos3 readings2 assignments3 discussion prompts1 plugin

4 videosTotal 16 minutes

Introduction: Collecting your own dataset1 minute
Collecting a dataset4 minutes
Interview: Advice for your first Machine Learning Project10 minutes
Summary1 minute

3 readingsTotal 35 minutes

Collecting a dataset15 minutes
Reflecting on your project10 minutes
What's next?10 minutes

2 assignmentsTotal 45 minutes

Preparing for your machine learning project15 minutes
Evaluating your machine learning project30 minutes

3 discussion promptsTotal 90 minutes

Your machine learning project30 minutes
Advice on data collection30 minutes
What have you learned?30 minutes

1 pluginTotal 180 minutes

Training a model using your dataset180 minutes

Build toward a degree

This course is part of the following degree program(s) offered by University of London. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Instructor

Instructor ratings

(1,088 ratings)

Prof Marco Gillies

University of London

14 Courses580,611 learners

Offered by

University of London

Learner reviews

5 stars
75.25%
4 stars
19.68%
3 stars
3.67%
2 stars
0.59%
1 star
0.79%

Showing 3 of 3536

Reviewed on Jun 16, 2020

Excellent introductory concept to Machine Learning. The course is easy-to-follow and clear to understand. The quiz at the end of each chapter also serves as a good recap to the understanding.

Reviewed on Jul 31, 2020

This course is very useful for beginners. I learned so many things about Machine Learning. Learned about different projects from experts , what they have done. It's a good course for everyone.

Reviewed on Jun 3, 2020

It was a great time to learn new things from this course. This course has helped me understanding the fundamentals of Machine Learning. Due to this Course I am clear about the ideas.

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