Mathematics for Machine Learning
Linear Algebra
Mathematics for Machine Learning,
In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.
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Rating | 4.5★ based on 874 ratings |
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Length | 6 weeks |
Effort | 5 weeks of study, 2-5 hours/week |
Starts | Jun 19 (41 weeks ago) |
Cost | $49 |
From | Imperial College London via Coursera |
Instructors | David Dye, Samuel J. Cooper, A. Freddie Page |
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
data science
I audited the course to gain practical experience and notation reading skills for my data science studies.
Overall, loved this course and highly recommend it to data science enthusiasts taking baby steps towards deep learning.
I would give this course 5 stars for the fact that in five weeks, the course is able to go through perhaps a semester or two or three of Linear Algebra (LA), and how LA fits into data science.
Because I had done a couple other courses on LA relatively recently, some these arcane LA concepts were grasped with some, but not too much, effort.If you are even just a little familiar with LA, this course will give you a good foundation for the LA relative to data science.
(I'm an old-timer, reviewing this material to get up to speed on Machine Learning and Data Science.)
This course can help anyone build a good foundation in Linear Algebra very nice It's a great foundation course for anyone who wants start their journey in Data Science.The content is relevant to ML applications.
The course for every engineer who want to refresh math skills before trying data science.
A Very good course to get knowledge of linear algebra and its applications to ML and data science great course.
Overall, this course would be a really good starting point for anyone willing to start their journey in the world of Machine Learning and Data Science.
The stated goal of the course is to provide a sufficient base of knowledge in linear algebra for applied data science i.e.
(a) to teach linear algebra without gory proofs or endless grinding through algorithms by hand and (b) to foreground geometric interpretations of linear algebra that can be recalled for many data science techniques and visualized with common data science tools.
Carefully explain the core concept in linear algebra and links with computational method :) Clearly presented and engaging through the course's focus on Data Science problems.
It's better to read some books as well when take the course Perfect for those who thinking of starting Machine Learning and Data Science or AI Excellent, but for the pagerank part, the instructor teaches a little bit fast.
changing the basis and reflecting plane helped me lot I took this course as a review for my data science curriculum.
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page rank
Also, the page rank case is quite cool and thought provoking.
It was very very difficult to follow the page rank video.
The course seems to ask for some faith that various concepts introduced earlier in the course will be united by the end, but never makes good; opting instead for a kind of sleight of hand: having students implement the Page Rank algorithm with the intention that this will draw together the core concepts of the course.
The programming exercises such as Reflecting Bear and Page Rank have been curated well.
clearly explain all the key concepts in la нормальный курс, базовые вещи по линейной алгербре рассказаны, объем небольшой но то что рассказывается - весьма доступно I am feeling like something is missing during the last part of the course when it comes to Page Rank Algorithm.
Jitesh Tripathi i expected a practical mathematic approach rather than only mathematical approach.but page rank algo is good Very quick thorough covering of the fundamentals of Linear Algebra as applicable to ML.It has some decent Python stuff too.
For instance, it's unclear how the characteristic equation comes about (by assuming that the characteristic matrix does not have an inverse) and also why the page rank matrix is setup the way it is.
Eigenvalues and eigenvectors while explained conceptually very well, the jump to page rank and transformations using them was bit hand wavy.
Power Iteration method for the Page Rank Algorithm should be more specific and clear.
Easy to get the concepts of Vectors, Eigen Values, Eigen Vectors, and Page Rank algorithm.
The Page Rank assignment for example was great.
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basics of linear algebra
I can't wait to complete the entire specialization It's been excellent so far as the instructor has mixed the basics of linear algebra and real world examples to highlight the application in machine learning.
Practically going through the basics of linear algebra.
This course is little challenging if one has not revised Linear Algebra before, but quite interesting and fun given the examples and utility only after learning the basics of linear algebra elsewhere and then attempting this one.
Good for studying basics of linear algebra great course Very much interesting course and found helpful also.Teachers gave a very nice explanation.Thank You Teachers I am in last week and till now it is the best.
Reviewing the material and writing it down requires rewatching the lectures Very Good not well explained...Rather than this go for khan's academy Best course to understand the basics of linear algebra before getting started with Machine Learning or AI First quiz doesn’t work properly.
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applications in machine learning
This was more inclined towards applications in machine learning.
All the best Good video & subtitles for non-english speaker, practical examples, good introduction to linear algebra A very nice introduction of linear algebra from applications in machine learning perspective.
Overall, I was able to overcome the challenged through self learning, understand the concepts well, and appreciate the applications in machine learning.
Examples of non-Euclidean spaces and their applications in machine learning not provided (geometrical deep learning on graphs for example).
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matrices and vectors
This course changed the view I look at matrices and vectors.
Mainly explains how to operate with matrices and vectors.
Great lectures and wonderful scrutiny of matrices and vectors.
It finally starts making sense why we use matrices and vectors.
After using matrices and vectors for decades in my work, I have finally gained some intuition about what the dot-product operation, determinant and eigen-vectors actually represent.
Highly recommend it if you focus more on calculation without knowing the meaning behind matrices and vectors in your past linear algebra journey.
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rather than
This course is much more focused on the meaning and usefulness of these things, rather than just learning how to do the maths.
The particular highlights are the use of geometric perspectives to give intuition rather than just labouring through the mathematics.
Great Course, exceptional in every way, gives you practice drill down some of the concepts, and handy programming assignments that are fun to work with, while not a complete refresher the course is good enough to grasp essence of linear algebra to build intuitive math, rather than classical way of teaching.
Thus the course should be considered a brief glance at linear algebra, rather than a proper course on the subject.
Focused on the geometrical view to look at the linear algebra rather than hand-calculations.
Intuitive explanations rather than jargons, very enjoyable course excelente curso, me gustaría que se complementara con programación.
The teaching and explanation of maths is actually great but rather than simply teach you mathematics, the course instead forces you into programming assignments.
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imperial college london
The content of this course are well prepared, this is such a masterpiece from Imperial College London.
Thanks Coursera and Imperial College London for this awesome course.
This course is very well designed, I loved it!I would like to see an Imperial College London design a fully fledged Machine Learning Course.
The instructor is really great (and neat), communicates the ideas really well and if Imperial College London is ranked that high worldwide, it's definitely because they hire professors this good.
I am so looking forward to starting it over again here shortly after I finish these next 2 fundamental prerequisites as I regard themKind regards,JeanPierre (John Fisher) great course for an intro to linear algebra....thanks Imperial College London!
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years ago
I studied linear algebra at university, more than 10 years ago so I had some memories of the topic.
I wish my university mathematics was taught like this 15 years ago.
This is an excellent refresher of vectors and linear algebra, and although I did it years ago in college I still found some new insights from doing this course.
The course by no means replace a full semester course on linear algebra, but it´s useful for those who had already had a course on L.A. years ago and want a refresher.
I didn't know how much I needed this reinforcement, as I had my linear algebra near to 30 years ago.
Excellent Course, I remembered the linear algebra that I saw in school more than 26 years ago (I studied applied mathematics and switched to Actuaria), but now with examples related to DataScience.
I am an immunologist with a little background in machine learning and my last studies in mathematics taken 15 years ago, but this course has the perfect level I need.
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real world
I wish there were more programming exercise based assignments and less hand-calculation based quizzes to make it close to real world applications.
Eigen vector concept was not clearly explained as to how it applies to real world.
Lot of the concepts seemed glossed over and could have used more guided practice and/or linkages to real world problems.
One of the most concise and yet complete courses on Linear algebra in the light of its practical application in the real world and machine learning greate course!
I also appreciated the complementary python exercises and the effort to put the material into a context of a real world application.
However, I would say perhaps there could be more challenging questions on the real world applications of linear algebra in machine learning followed by in-depth step-by-step solutions in order to really get the application-based learning inside your meat.
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eigen values
Coming back to this course online, it really did help me get a much better understanding of concepts like dimensionality, basis, eigen values and eigen vectors.
Understand the concepts of eigen values and eigen vectors and got understanding of how google pagerank works.
Could have had a little more about eigen vectors and eigen values The course content is good, but the programming assignment is too easy.
A new way of looking at eigen values and vectors, every engineer should do this course.It will help developing strong fundamentals for machine learning.
1.Need more clarity on calculating Eigen vectors using back substitution of Eigen values.2.
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exactly what
Quality of the recording is impressive, content what exactly what I was looking for.
This course is EXACTLY what I needed - a demonstration of the ideas of linear algebra and how to implement those ideas Great primer.
Gave a nice intuition to the subject and that was exactly what I needed Challenging course.
Great approach, teaching the intuition of mathematics, this is exactly what I was looking for!
Exactly what I needed.
It was exactly what I was expecting.
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david dye
Very good linear algebra intro/refresher David Dye is excellent!
Especically his brilliant smile ,excited expression and body language which inspiring me a lot!表白David Dye,比心! Brilliant brush up course.
David Dye the best!
very bad, everything is not clear David Dye is awesome!
At one stage David Dye offhandedly mentions soh-cah-toa... and that really sums up a lot of what is required in terms of mathematical maturity - high school maths at a reasonable level.Those that undertake the course should be assisted by referring to additional materials when they feel things are a bit of a struggle, I did, and this greatly helped, although my Maths was around UK high school level (in Algebra and Trig).Overall first class and easily manageable with a little work!
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Rating | 4.5★ based on 874 ratings |
---|---|
Length | 6 weeks |
Effort | 5 weeks of study, 2-5 hours/week |
Starts | Jun 19 (41 weeks ago) |
Cost | $49 |
From | Imperial College London via Coursera |
Instructors | David Dye, Samuel J. Cooper, A. Freddie Page |
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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