Howard University Partners with Coursera to Provide Online Educational Content
WASHINGTON –Howard University announces a partnership with Coursera to provide online educational content to build a job-relevant course catalog, covering business, technology, and data science. Later this year, Howard will launch two Coursera courses leading to certificates: Information Systems for Business and Linear Algebra for Data Science.
“I am really excited about this partnership with Coursera, one of the preeminent purveyors of educational content in the 21st-century learning landscape, which will help us spread the vision and mission of Howard University beyond our campus,” said President Wayne A. I. Frederick, who is a member of Coursera’s University Advisory Board. “Historically, there has been a disconnect between companies and top-level talent from underrepresented communities who don’t have the same access and resources as other job seekers. These kinds of partnerships can help enhance opportunities for people of color by aligning their education with the needs of businesses.”
In 2020, Coursera strengthened their commitment to address systemic racism through learning. The company focused on creating social justice and anti-racism content and elevating Black voices in their instructor community.
“Howard University has a distinguished legacy in educating Black communities,” said Betty Vandenbosch, chief content officer at Coursera. “I am thrilled that our new partnership will enable learners around the world to gain job-relevant skills from Howard’s leading experts. Together, we are increasing access to top-quality education and empowering millions of learners with content that can truly transform their minds, their lives and the world.”
The Howard University courses offered through Coursera will give learners from all over the world a taste of the Howard experience with training provided by the institution’s world-class faculty for a nominal monthly fee.
“We anticipate that it will take Howard faculty four to five months to develop the classes that we will offer on Coursera, and they will be packaged into Coursera’s exceptional certificate programs,” said Barron Harvey, Ph.D., associate provost for academic innovation and strategic initiatives. “The best part is that students won’t have to wait until the beginning of a semester to join the courses because they will be available to take at your own pace and timing.”
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Howard Forward Strategic Pillars
Mathematics for Machine Learning
Below are the top discussions from Reddit that mention this online Coursera specialization from Imperial College London.
For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to h...
Eigenvalues And EigenvectorsPrincipal Component Analysis (PCA)Multivariable CalculusLinear AlgebraBasis (Linear Algebra)Transformation MatrixLinear RegressionVector CalculusGradient DescentDimensionality ReductionPython Programming
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Professor of Metallurgy
and 16 more instructors
Imperial College London
This specialization includes these 3 courses.
Reddit Posts and Comments
1 posts • 111 mentions • top 50 shown below
Anyone taken Mathematics for Machine Learning Specialization by Imperial London College on Coursera?
I'm thinking about auditing the Mathematics for Machine Specialization by Imperial London College. Does anyone have experience with this course/professors/college? I think it's relatively new, but I see that others have been in it for the past month or so (maybe as beta testers?). Either way, if anyone has experience with it, I'd love to hear if it's worthwhile.
I'll update as I go through the course as well.
Mathematics for Machine Learning
I want to get started on machine learning, but I have some doubts about the prerequisites
Basically, I want to learn enough to get a "feel" of how its like to develop in the area, to see if thats what I want to specialize myself in. I searched around for a bit and saw that some of the prerequisites for understanding machine learning are linear algebra and multivariate calculus. Because of that I was thinking of enrolling myself in this specialization in coursera:
But I am not sure exactly if thats enough, and thats what I would like to know here. However, if its not, I would like to know what else I would need to learn regarding these specific fields, keeping in mind that I am not trying to become a researcher or anything of the sort, rather I am just exploring the area. Thanks in advance.
There is https://www.coursera.org/specializations/mathematics-machine-learning course series on coursera. If that is too basic for you then read the book 'all of statistics' from Larry Wasserman and do all the problems in the book.
Also if you just want to learn the basic math, without any difficulty of the math specialist or the math major, coursera has enough for you, no need to bother to learn from mat223/224
Best resource/course to learn machine learning maths?
So I'm doing a machine learning course, and it shows me the algorithms, I have a rough idea of why it works but I want to understand on a better level than knowing when to use it. Is there a comprehensive course that teaches the maths more in depth? I was looking as "Mathematics for machine learning specialization" on Coursera. Have any of you guys taken this course?
Coursera has a couple of classes that might help...
Mathematics for Machine Learning | Imperial College London https://www.coursera.org/specializations/mathematics-machine-learning
Course: Mathematics for machine learning
If you prefer being spoonfed then choose the coursework/non-thesis option. If you have 3 months before the program starts, you should do Mathematics for Machine Learning Specialization on Coursera to refresh your math. After this, you’re all set!
The only path to getting good at something is practicing it! It helps to start with smaller things and explore around, so a bit of spreading yourself thin at first isn't a waste of time at all!
The first time I launched a webapp I did the whole nine and built it from the ground up on a LAMP stack - way more difficult than it needed to be. Since you're just practicing and playing around, there's no need to worry too much about backend and hosting, so an easy start on web stuff in Python is Flask and learn the rest from there. The last thing I built used that and it was far easier, I had workable prototypes in a couple days and was able to iterate more easily from there.
Coursera has some classes on ML mathematics that utilize Python as well! Kaggle stuff - I think - can wait until you feel more comfortable with what's going on. Since you're a "first principles" kind of learner (me too!) it'll probably feel better to build up from basics rather than backtrack from frameworks.
You might be interested in the Mathematics for Machine Learning from Imperial College London on Coursera
For machine learning in Python and R: https://www.udemy.com/machinelearning/ And after if you're interested in deep learning: https://www.udemy.com/deeplearning/ Andrew ng course is very interesting for the theory behind the algorithms, if you need to train yourself in mathematics you can also follow this course: https://www.coursera.org/specializations/mathematics-machine-learning
No Brasil eu ainda não tive contato com pessoas de humanas trabalhando com DS. O que eu sei é que no US tem muita gente migrando pra essa área, e muitos fazendo sucesso.
Eu respondi pra outra pessoa que interdisciplinaridade é sempre maravilhosa. Nesse caso eu considero mais ainda, dado que leis de proteção aos dados estão surgindo em muitos países (incluindo aqui).
Não acho que seja necessário uma formação técnica, desde que você tivesse conhecimento nos tópicos básicos da área.
No seu caso, aconselho começar relembrando matemática do ensino médio. Depois pega esse curso aqui:
Pra você não desmotivar, vai fazendo em paralelo os cursos da Alura. É uma ótima porta de entrada.
Depois disso provavelmente você já vai ter uma base pra saber sozinho onde atacar. Na dúvida, consulte os livros que deixei no post. Eles são ótimos materiais de consulta, além de serem ótimos guias.
There's also a Coursera specialization by the authors of the book, but I think you're better off with the book because the courses are far too short and easy.
I'd recommend being familiar with matrices and matrix math. Coursera has a nice series that gives a solid foundation if you are interested in ML coursework: Math for Machine Learning
This user spams their linksynergy redirecting referral links all over.
For those genuinely interested in seeing what's behind the curtain, here's a direct link to the content:
I did one course from this https://www.coursera.org/specializations/mathematics-machine-learning specialization. Was pure linear algebra and python coding. They cover matrices transformation in space pretty good as well as operations on vectors. Pretty intense though. I have an applied math degree but still wasn’t relaxed. Buts that’s a good thing. Don’t know what’s there next. Calculus on Coursera is free and not too bad as well.
Definitely not difficult (though I do wonder what's their deal with Abelian groups, uh). However, if to find them hard, you may have a look at the Coursera specialization and ask questions on the forum. I'm not sure that's worth the time investment (I'd rather follow the 2 fast.ai courses), but YMMV.
This one might be worth looking into as well:
You might like to check out the specialization offered by the Imperial College on Coursera. It is free to audit the courses and covers all the subjects required for data science. https://www.coursera.org/specializations/mathematics-machine-learning
If you've already learned single variable calculus before, then I recommend Khan Academy. It's less rigorous but it's a good refresher. As for multivariable calculus (Machine Learning probably involves linear algebra and differential equations depending on the application), you'll need to put in the work. These are usually subjects covered over at least a semester each in college and are fairly demanding classes.
If you're more interested in learning just what you need for ML and not worrying about the theory behind the concepts, another commenter recommended the Maths for ML specialization on Coursera.
I’m halfway through this Imperial College course (on Coursera) called Mathematics for Machine Learning and found it to be very useful. The Linear Algebra part is very good and so is the second part multi variate calculus. Haven’t started the third part on PCA yet. https://www.coursera.org/specializations/mathematics-machine-learning
Here you go mate https://www.coursera.org/specializations/mathematics-machine-learning
Check this course out — https://www.coursera.org/specializations/mathematics-machine-learning .
+1 - Very well written. It also has an accompanied course @ Coursera
I am in the same boat. I did the Introduction to Computer with Python from GTx (edX) and I am currently doing the Machine Learning Core from Microsoft (edX). Everybody also suggests the math course from Imperial College (https://www.coursera.org/specializations/mathematics-machine-learning) so I am doing that one too. For programming itself I would stay away from Data Camp. It is better to learn how to use the language for data analysis. I am in the DC area too and would be interested in forming an study group.
I'd counter argue that machine learning is edging towards a more specialised area of CS. Don't get me wrong, it's growing in popularity but it's not something most developers go into, at least not to begin with.
Even so, there are many courses available online for specialised areas like that above. Coursera for example offers a course particularly on Mathematics for Machine Learning.
I'm willing to bet the majority of developers out there are rusty in areas like "Multivariable Calculus" or "Linear Algebra". For most, it's not something that's needed for most jobs out there.
Is that this course of the same name? I took that course (really, specialization) but don’t remember them ever mentioning this book....
How about this?
The MML book suggested by others is a good resource, but I think the coursera specialization might be to a large extent covered in the courses you already passed. But from my experience learning the topics stand alone is not the most effective, or at least doesn't compare to to learning them on-demand. What I mean is that the whole topic of MML, similar to the book, is very vast and going through everything could result in poor retention. On the other hand, once you know the basics, goal-driven and motivated learning can be ridiculously effective and efficient. If you're interested to learn more about ML topics, start from there. When you need better understanding of a Mathematical topic come to resources like MML. For me, that always worked best in learning.
Other than my stats text books no, lol but a great crash course I took was on Coursera. From AMII school called “Intro to Applied Machine Learning”. It gives a great intro to the type of math you’ll need without getting too complicated. If you want something a little more technical and complicated, imperial college has a good program on just the math on Coursera. Link: https://www.coursera.org/specializations/mathematics-machine-learning. Another program I did was the IBM data science professional cert. touched on a bunch of different things from the math to coding it in Python. It was a great crash course but wasn’t a big fan tbh. I’d start with AMII and Imperial college. In that order. People say Stanford has a very good course/program on Coursera. I plan to take that next. Andrew Ng put it together. Basically, if you’ve taken a few stats courses, linear algebra, multivariate calculus, this stuff will be a refresher for you.
Coursera specialization Mathematics For Machine Learning is a warm recommendation.
Third course is about PCA, which is a bit of a specific domain, but first two courses are great imho.
> Not much math in the life of ML engineer. Their main task is to implement ideas of others into actual working, scalable code.
I want to be an ML engineer. I have finished Coursera Course and now planning to do Deep Learning AI course. And also this specialization - Mathematics for Machine Learning, would that be enough to make a career switch? (currently I work as a backend dev)
Stolen content: https://www.coursera.org/specializations/mathematics-machine-learning
Look into the Mathematics for Machine Learning Specialization by Imperial College London on Coursera, which covers some of the math you may need. Imperial College London is also going to be offering an online master's program in machine learning through Coursera.
Also see deeplearning.ai, which has a Coursera specialization on deep learning.
I highly recommend Mathematics for Machine Learning from Imperial College London:
It's among the most concise ways to get the linear algebra and calculus you'll need that I've seen. Doing the first two courses should give enough background for ML, AI, and (probably) DL.
A motivated and mathematically inclined student could finish those in 2-3 weeks total. Maybe 4-6 if the material is new to someone.
The other math piece that's needed for these types of courses is probability. I don't think it's unreasonable to consider these things prerequisites without offering a course in them.
If I were to lobby for another OMSCS ML course (other than the aforementioned DL), it would be CS 7545, Machine Learning Theory.
Although I took Linear Algebra and multi-variate calc many years ago, I plan on doing this before applying to OMSCS: https://www.coursera.org/specializations/mathematics-machine-learning and https://www.coursera.org/specializations/statistics
One introductory college course on each of these topics is all you need. Rather than loading your head with a lot of theory, learn to grow a proper understanding of fundamentals. Have crystal clear intuitions of fundamental topics in Lin Alg. and Calculus.
You will have the computer to do calculation-intensive terms anyway, so try to gain absolutely clear intuitions and a proper low-level (detailed) understanding.
Learn from here- * Khan Academy (basic theory, manipulation) * 3blue1brown (crystal clear intuition) * Mathematics for Machine Learning Specialization, Imperial College London (intuition, concepts, and implementing math in code)
If you have the opportunity to take a good college-level introductory course, take that. The above resources are great whether you take a course or not.
(I am from a Physics background, and hence, taken multiple courses in these topics, and I will say that I learned more using the above sources. Khan Academy is for HS and I go there when I need a refresher on something.)
Do you think this course - https://www.coursera.org/specializations/mathematics-machine-learning covers all the necessary math for the ML course? Would you recommend any other courses or books to get familiar with the kind of math required for the ML course?
Also, do you think taking a purely mathematical course of this program (like Advanced Linear Algebra or Optimization) before taking the ML course might be helpful?
If you want to learn a few neat tricks in Python, just read the documentation for the common data science libraries (Numpy, Pandas, Scipy, scikit-learn, Seaborn, Matplotlib). They are very well-documented and come with quick tutorials. That would teach you more about them than any of those courses ever would.
In terms of machine learning, I'd stay stay away from Coursera and Udemy and pick up a book. They are both meant to appeal to a general audience which means they leave out the math side of things and you don't fully get a sense of what goes on in these ML algorithms. Here's an example of how watered down these Coursera courses are:
Here is a Coursera specialization in Math for Machine Learning
Here is a book written by the same exact people with the same premise. Notice how much more in depth the book goes. It gives you a far better understanding of how math relates to machine learning and covers many more interesting topics.
It isn't just them though. All of these courses do it. If you're comfortable with math/stats, I'd definitely pick up a book.
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A line segment between points is given by the convex combinations of those points; if the "points" are images, the line segment is a simple morph between the images.
Given a photo of a whiteboard taken at an angle, synthesize a perspective-free view of the whiteboard.
The same transformation can be used in using a Wiimote to make a low-cost interactive whiteboard or light pen (due to Johnny Chung Lee).
Error-correcting codes are used, e.g., by cellphones to preserve data transmitted over a noisy channel while maintaining high throughput.
522253825433285668885771662040104167 = 891428822186035241∙585861498344390287
Factoring an integer is a hard computational problem (and the RSA cryptosystem depends on it being hard). At the core of the most sophisticated integer-factoring algorithms is a simple problem in linear algebra.
Blurring an image is a simple linear transformation.
Searching within an audio clip
Searching for one audio clip within another can be formulated as a convolution. A convolution can be computed very quickly using the Fast Fourier Transform.
Searching within an image
Convolution can also be done in two dimensions, enabling one to quickly search for a subimage within an image.
Audio and image compression
Compression of audio and images aids efficient storage and transmission. Lossy compression techniques such as those used in MP3 (audio) and JPEG (images) are based in part on linear algebra,
e.g. wavelet transform and Fourier transform.
100% original size
40% original size
10% original size
A "classical" approach to face detection is eigenfaces, a technique related to principal component analysis.
2d graphics transformations
Simple transformations that arise in graphics such as rotation, translation, and scaling can be expressed using matrices.
Lights Out is a puzzle in which you must select the correct buttons to push in order for all the lights to go out. Finding a solution can be expressed as a problem in linear algebra.
Minimum-weight spanning forest
Finding the minimum-weight spanning tree of a graph can be interpreted as the problem of finding a minimum-weight basis for a vector space
derived from the graph.
A nice drawing of a graph can be obtained from eigenvectors of a related matrix.
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