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

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
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?

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Our top 15 tweets

A #prodmgmt thread 👇

https://t.co/Yv854Sd3P3


https://t.co/sXaMH1bZ9m


https://t.co/5X7bOTsS7m


https://t.co/w1y6LTtPS2
New from me:

I’m launching my Forecasting For SEO course next month.

It’s everything I’ve learned, tried and tested about SEO forecasting.

The course: https://t.co/bovuIns9OZ

Following along 👇

Why forecasting?

Last year I launched
https://t.co/I6osuvrGAK to provide reliable forecasts to SEO teams.

It went crazy.

I also noticed an appetite for learning more about forecasting and reached out on Twitter to gauge interest:

The interest encouraged me to make a start...

I’ve also been inspired by what others are doing: @tom_hirst, @dvassallo and @azarchick 👏👏

And their guts to be build so openly in public.

So here goes it...

In the last 2 years I’ve only written 3 blog posts on my site.

- Probabilistic thinking in SEO
- Rethinking technical SEO audits
- How to deliver better SEO strategies.

I only write when I feel like I’ve got something to say.

With forecasting, I’ve got something to say. 💭

There are mixed feelings about forecasting in the SEO industry.

Uncertainty is everywhere. Algorithm updates impacting rankings, economic challenges impacting demand.

It’s difficult. 😩
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.

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Master Thread of all my threads!

Hello!! 👋

• I have curated some of the best tweets from the best traders we know of.

• Making one master thread and will keep posting all my threads under this.

• Go through this for super learning/value totally free of cost! 😃

1. 7 FREE OPTION TRADING COURSES FOR


2. THE ABSOLUTE BEST 15 SCANNERS EXPERTS ARE USING

Got these scanners from the following accounts:

1. @Pathik_Trader
2. @sanjufunda
3. @sanstocktrader
4. @SouravSenguptaI
5. @Rishikesh_ADX


3. 12 TRADING SETUPS which experts are using.

These setups I found from the following 4 accounts:

1. @Pathik_Trader
2. @sourabhsiso19
3. @ITRADE191
4.


4. Curated tweets on HOW TO SELL STRADDLES.

Everything covered in this thread.
1. Management
2. How to initiate
3. When to exit straddles
4. Examples
5. Videos on