Recently, I shared a list of some courses that were useful in my transition to machine learning.

While I took most of the courses in person, there are some alternative online courses you can check out.

Here is a thread of a few interesting online courses based on the list:

⭐️ Linear Algebra ⭐️

A classical online course by Professor Gilbert Strang based on his popular textbook "Introduction to Linear Algebra". Learn about matrix theory and systems of equations.

https://t.co/GarhvVxXhG
⭐️ Introduction to Complex Analysis ⭐️

Learn about the geometry of complex numbers.

https://t.co/DgMuMCgAhr
⭐️ Differential Calculus ⭐️

Pay close attention to the chain rule as it's heavily referenced in machine learning, specifically when discussing optimization. This course is part of a specialization called MathTrackX. I recommend checking that as well.

https://t.co/H4hK05t44b
⭐️ Information Theory ⭐️

When you are working with machine learning algorithms applied to data you are dealing with information processing which in essence relies on ideas from information theory such as entropy. This course should provide the basics.

https://t.co/ETeMiwTry1
⭐️ Data Mining Specialization ⭐️

The courses in this specialization provide a great overview of data mining techniques used for structured and unstructured data.

https://t.co/oGzoOGOMnU
⭐️ Algorithms ⭐️

In machine learning, we are programming sophisticated algorithms and it's important to understand key concepts in this subject before jumping straight into ML algorithms. In general, an Algorithms course builds a strong CS foundation.

https://t.co/bdlXphoJud
⭐️ Mathematics for Machine Learning Specialization ⭐️

Note: Includes courses for multivariate calculus and linear algebra. One of my favorite courses due to the quality of lectures and focused topics.

https://t.co/3Uf3iuni3z
⭐️ Statistics with Python Specialization ⭐️

This course is focused on the basics of statistics which is important when dealing with uncertainties, modeling, inference, etc. Although the courses focus on Python, there are other options using R as well.

https://t.co/yZOUQBTZNI
⭐️ An Intuitive Introduction to Probability ⭐️

Probability can become a difficult topic but it's a core concept of building probabilistic prediction models. This course can provide an intuitive introduction to core topics like conditional probability.

https://t.co/sGirM58T9p
Exposure to topics in these courses can help improve your knowledge/intuition needed to transition to machine learning.

The list is not exhaustive so if you have any courses you recommend, please reply below. In time, I will prepare a better and more focused ramping up guide.

More from elvis

The past month I've been writing detailed notes for the first 15 lectures of Stanford's NLP with Deep Learning. Notes contain code, equations, practical tips, references, etc.

As I tidy the notes, I need to figure out how to best publish them. Here are the topics covered so far:


I know there are a lot of you interested in these from what I gathered 1 month ago. I want to make sure they are high quality before publishing, so I will spend some time working on that. Stay


Below is the course I've been auditing. My advice is you take it slow, there are some advanced concepts in the lectures. It took me 1 month (~3 hrs a day) to take rough notes for the first 15 lectures. Note that this is one semester of

I'm super excited about this project because my plan is to make the content more accessible so that a beginner can consume it more easily. It's tiring but I will keep at it because I know many of you will enjoy and find them useful. More announcements coming soon!

NLP is evolving so fast, so one idea with these notes is to create a live document that could be easily maintained by the community. Something like what we did before with NLP Overview: https://t.co/Y8Z1Svjn24

Let me know if you have any thoughts on this?
Amazing resources to start learning MLOps, one of the most exciting areas in machine learning engineering:



📘 Introducing MLOps

An excellent primer to MLOps and how to scale machine learning in the enterprise.

https://t.co/GCnbZZaQEI


🎓 Machine Learning Engineering for Production (MLOps) Specialization

A new specialization by https://t.co/mEjqoGrnTW on machine learning engineering for production (MLOPs).

https://t.co/MAaiRlRRE7


⚙️ MLOps Tooling Landscape

A great blog post by Chip Huyen summarizing all the latest technologies/tools used in MLOps.

https://t.co/hsDH8DVloH


🎓 MLOps Course by Goku Mohandas

A series of lessons teaching how to apply machine learning to build production-grade products.

https://t.co/RrV3GNNsLW

More from Education

You asked. So here are my thoughts on how osteopathic medical students should respond to the NBOME.

(thread)


Look, even before the Step 2 CS cancellation, my DMs and email were flooded with messages from osteopathic medical students who are fed up with the NBOME.

There is *real* anger toward this organization. Honestly, more than I even heard about from MD students and the NBME.

The question is, will that sentiment translate into action?

Amorphous anger on social media is easy to ignore. But if that anger gets channeled into organized efforts to facilitate change, then improvements are possible.

This much should be clear: begging the NBOME to reconsider their Level 2-PE exam is a waste of your time.

Best case scenario, you’ll get another “town hall” meeting, a handful of platitudes, and some thoughtful beard stroking before being told that they’re keeping the exam.

Instead of complaining to the NBOME, here are a few things that are more likely to bring about real change.
The outrage is not that she fit better. The outrage is that she stated very firmly on national television with no caveat, that there are no conditions not improved by exercise. Many people with viral sequelae have been saying for years that exercise has made them more disabled 1/


And the new draft NICE guidelines for ME/CFS which often has a viral onset specifically say that ME/CFS patients shouldn't do graded exercise. Clare is fully aware of this but still made a sweeping and very firm statement that all conditions are improved by exercise. This 2/

was an active dismissal of the lived experience of hundreds of thousands of patients with viral sequelae. Yes, exercise does help so many conditions. Yes, a very small number of people with an ME/CFS diagnosis are helped by exercise. But the vast majority of people with ME, a 3/

a quintessential post-viral condition, are made worse by exercise. Many have been left wheelchair dependent of bedbound by graded exercise therapy when they could walk before. To dismiss the lived experience of these patients with such a sweeping statement is unethical and 4/

unsafe. Clare has every right to her lived experience. But she can't, and you can't justifiably speak out on favour of listening to lived experience but cherry pick the lived experiences you are going to listen to. Why are the lived experiences of most people with ME dismissed?
When the university starts sending out teaching evaluation reminders, I tell all my classes about bias in teaching evals, with links to the evidence. Here's a version of the email I send, in case anyone else wants to poach from it.

1/16


When I say "anyone": needless to say, the people who are benefitting from the bias (like me) are the ones who should helping to correct it. Men in math, this is your job! Of course, it should also be dealt with at the institutional level, not just ad hoc.
OK, on to my email:
2/16

"You may have received automated reminders about course evals this fall. I encourage you to fill the evals out. I'd be particularly grateful for written feedback about what worked for you in the class, what was difficult, & how you ultimately spent your time for this class.

3/16

However, I don't feel comfortable just sending you an email saying: "please take the time to evaluate me". I do think student evaluations of teachers can be valuable: I have made changes to my teaching style as a direct result of comments from student teaching evaluations.
4/16

But teaching evaluations have a weakness: they are not an unbiased estimator of teaching quality. There is strong evidence that teaching evals tend to favour men over women, and that teaching evals tend to favour white instructors over non-white instructors.
5/16

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#24hrstartup recap and analysis

What a weekend celebrating makers looks like.

A thread

👇Read on

Let's start with a crazy view of what @ProductHunt looked like on Sunday

Download image and upload

A top 7 with:
https://t.co/6gBjO6jXtB @Booligoosh
https://t.co/fwfKbQha57 @stephsmithio
https://t.co/LsSRNV9Jrf @anthilemoon
https://t.co/Fts7T8Un5M @J_Tabansi
Spotify Ctrl @shahroozme
https://t.co/37EoJAXEeG @kossnocorp
https://t.co/fMawYGlnro

If you want some top picks, see @deadcoder0904's thread,

We were going to have a go at doing this, but he nailed it.

It also comes with voting links 🖐so go do your


Over the following days the 24hr startup crew had more than their fair share of launches

Lots of variety: web, bots, extensions and even native apps

eg. @jordibruin with
I hate when I learn something new (to me) & stunning about the Jeff Epstein network (h/t MoodyKnowsNada.)

Where to begin?

So our new Secretary of State Anthony Blinken's stepfather, Samuel Pisar, was "longtime lawyer and confidant of...Robert Maxwell," Ghislaine Maxwell's Dad.


"Pisar was one of the last people to speak to Maxwell, by phone, probably an hour before the chairman of Mirror Group Newspapers fell off his luxury yacht the Lady Ghislaine on 5 November, 1991."
https://t.co/DAEgchNyTP


OK, so that's just a coincidence. Moving on, Anthony Blinken "attended the prestigious Dalton School in New York City"...wait, what? https://t.co/DnE6AvHmJg

Dalton School...Dalton School...rings a

Oh that's right.

The dad of the U.S. Attorney General under both George W. Bush & Donald Trump, William Barr, was headmaster of the Dalton School.

Donald Barr was also quite a


I'm not going to even mention that Blinken's stepdad Sam Pisar's name was in Epstein's "black book."

Lots of names in that book. I mean, for example, Cuomo, Trump, Clinton, Prince Andrew, Bill Cosby, Woody Allen - all in that book, and their reputations are spotless.