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Mathematics for Machine Learning

PCA

This course is a part of Mathematics for Machine Learning, a 3-course Specialization series from Coursera.

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms.

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Imperial College London

Rating 3.3 based on 140 ratings
Length 5 weeks
Effort 4 weeks of study, 4-5 hours/week
Starts Sep 16 (9 weeks ago)
Cost $69
From Imperial College London via Coursera
Instructor Marc P. Deisenroth
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming Mathematics
Tags Data Science Machine Learning Math And Logic

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What people are saying

According to other learners, here's what you need to know

programming assignments in 17 reviews

Programming assignments are really difficult and at many points frustrating.

Even though the instructor seems immensely knowledgeable he could work on delivering the material (which is more abstract than before to his credit) in a clearer manner.The programming assignments are great albeit a bit hard to troubleshoot at times.

You are then given programming assignments where at least half the effort is to try to understand what is being asked before you start to work to implement it.

There are some issues with the programming assignments and the lectures could do with some more practical examples.

I feel that the programming assignments were a bit more challenging and sometimes I was not too sure of what I was doing.

Programming assignments are a little difficult.

Programming assignments' quality is too bad to follow it.

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machine learning in 15 reviews

Very hard to follow, but you need to do it to understand machine learning very well.

I'm giving it only three stars because this is not what I expected, I signed up for this track to gain additional conceptual overview of how maths in many machine learning applications works on high level.

It was challenging but worth it to enhance the mathematic skills for machine learning.

Great capstone for the three-class Mathematics for Machine Learning series.

However, it just like the ingredients the math for machine learning will not be complete without attempting to this one.

its a good course to learn mathematics essential for machine learning This course demystifies the Principal Components Analysis through practical implementation.

That's a great online courses can help people have enough background to break into Machine Learning or Data science concise and to the point.

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first two courses in 9 reviews

This is not the case for the first two courses of this specialisation.

The first two courses in the Mathematics for Machine Learning specialisation are excellent - even amongst the best online or traditional maths courses I have taken.

This course brings together many of the concepts from the first two courses of the specialization.

Far more challenging than the first two courses.

This course does require you to have some prior experience, though, so if you are new to programming or linear algebra (not just the concepts but how to apply them) it's bets to take the first two courses with some additional help (maybe Khan academy or even MIT OCW.

Like the first two courses of the specialization, this course is shallow, shouldn't be anyone's introduction to the subject and is a refresher at best.

I've finished all the two previous courses in this specialization.I was shocked at seeing the content and programming assignments given to us.It was totally different.They expect a lot from us.Content is not up to the mark.First two courses was awesome.But this course is an exact opposite to the first two.Totally disappointed!!

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previous courses in 8 reviews

The previous courses were great at guiding, and in large part spoon feeding.

Unfortunately this course does is of much lower quality than the previous courses of the specialization.

This course is way more brutal than the two previous courses in the specializationIt is also very mathematically oriented, it lacks the graphics / animation / intuition that was given in the first two courses.However, if you make it, you indeed have a good understanding of PCA.

After taking/passing the two previous courses, this course is very disappointing.

I passed both of the previous courses.

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Coursera

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Imperial College London

Rating 3.3 based on 140 ratings
Length 5 weeks
Effort 4 weeks of study, 4-5 hours/week
Starts Sep 16 (9 weeks ago)
Cost $69
From Imperial College London via Coursera
Instructor Marc P. Deisenroth
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming Mathematics
Tags Data Science Machine Learning Math And Logic

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