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

More from Education

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).

You May Also Like

॥ॐ॥
अस्य श्री गायत्री ध्यान श्लोक:
(gAyatri dhyAna shlOka)
• This shloka to meditate personified form of वेदमाता गायत्री was given by Bhagwaan Brahma to Sage yAgnavalkya (याज्ञवल्क्य).

• 14th shloka of गायत्री कवचम् which is taken from वशिष्ठ संहिता, goes as follows..


• मुक्ता-विद्रुम-हेम-नील धवलच्छायैर्मुखस्त्रीक्षणै:।
muktA vidruma hEma nIla dhavalachhAyaiH mukhaistrlkShaNaiH.

• युक्तामिन्दुकला-निबद्धमुकुटां तत्वार्थवर्णात्मिकाम्॥
yuktAmindukalA nibaddha makutAm tatvArtha varNAtmikam.

• गायत्रीं वरदाभयाङ्कुश कशां शुभ्रं कपालं गदाम्।
gAyatrIm vardAbhayANkusha kashAm shubhram kapAlam gadAm.

• शंखं चक्रमथारविन्दयुगलं हस्तैर्वहन्ती भजै॥
shankham chakramathArvinda yugalam hastairvahantIm bhajE.

This shloka describes the form of वेदमाता गायत्री.

• It says, "She has five faces which shine with the colours of a Pearl 'मुक्ता', Coral 'विद्रुम', Gold 'हेम्', Sapphire 'नील्', & a Diamond 'धवलम्'.

• These five faces are symbolic of the five primordial elements called पञ्चमहाभूत:' which makes up the entire existence.

• These are the elements of SPACE, FIRE, WIND, EARTH & WATER.

• All these five faces shine with three eyes 'त्रिक्षणै:'.