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?

More from Education

Working on a newsletter edition about deliberate practice.

Deliberate practice is crucial if you want to reach expert level in any skill, but what is it, and how can it help you learn more precisely?

A thread based on @augustbradley's conversation with the late Anders Ericsson.

You can find my complete notes from the conversation in my public Roam graph:
https://t.co/Z5bXHsg3oc

The entire conversation is on

The 10,000-hour 'rule' was based on Ericsson's research, but simple practice is not enough for mastery.

We need teachers and coaches to give us feedback on how we're doing to adjust our actions effectively. Technology can help us by providing short feedback loops.

There's purposeful and deliberate practice.

In purposeful practice, you gain breakthroughs by trying out different techniques you find on your own.

In deliberate practice, an expert tells you what to improve on and how to do it, and then you do that (while getting feedback).

It's possible to come to powerful techniques through purposeful practice, but it's always a gamble.

Deliberate practice is possible with a map of the domain and a recommended way to move through it. This makes success more likely.
An appallingly tardy response to such an important element of reading - apologies. The growing recognition of fluency as the crucial developmental area for primary education is certainly encouraging helping us move away from the obsession with reading comprehension tests.


It is, as you suggest, a nuanced pedagogy with the tripartite algorithm of rate, accuracy and prosody at times conflating the landscape and often leading to an educational shrug of the shoulders, a convenient abdication of responsibility and a return to comprehension 'skills'.

Taking each element separately (but not hierarchically) may be helpful but always remembering that for fluency they occur simultaneously (not dissimilar to sentence structure, text structure and rhetoric in fluent writing).

Rate, or words-read-per-minute, is the easiest. Faster reading speeds are EVIDENCE of fluency development but attempting to 'teach' children(or anyone) to read faster is fallacious (Carver, 1985) and will result in processing deficit which in young readers will be catastrophic.

Reading rate is dependent upon eye-movements and cognitive processing development along with orthographic development (more on this later).

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These setups I found from the following 4 accounts:

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

Share for the benefit of everyone.

Here are the setups from @Pathik_Trader Sir first.

1. Open Drive (Intraday Setup explained)


Bactesting results of Open Drive


2. Two Price Action setups to get good long side trade for intraday.

1. PDC Acts as Support
2. PDH Acts as


Example of PDC/PDH Setup given