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
I have always emphasized on the importance of mathematics in machine learning.

Here is a compilation of resources (books, videos & papers) to get you going.

(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)

📘 Mathematics for Machine Learning

by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong

https://t.co/zSpp67kJSg

Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.


📘 Pattern Recognition and Machine Learning

by Christopher Bishop

Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.


📘 The Elements of Statistical Learning

by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie

Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...


📘 Probability Theory: The Logic of Science

by E. T. Jaynes

Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
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

Trending news of The Rock's daughter Simone Johnson's announcing her new Stage Name is breaking our Versus tool because "Wrestling Name" isn't in our database!

Here's the most useful #Factualist comparison pages #Thread 🧵


What is the difference between “pseudonym” and “stage name?”

Pseudonym means “a fictitious name (more literally, a false name), as those used by writers and movie stars,” while stage name is “the pseudonym of an entertainer.”

https://t.co/hT5XPkTepy #english #wiki #wikidiff

People also found this comparison helpful:

Alias #versus Stage Name: What’s the difference?

Alias means “another name; an assumed name,” while stage name means “the pseudonym of an entertainer.”

https://t.co/Kf7uVKekMd #Etymology #words

Another common #question:

What is the difference between “alias” and “pseudonym?”

As nouns alias means “another name; an assumed name,” while pseudonym means “a fictitious name (more literally, a false name), as those used by writers and movie

Here is a very basic #comparison: "Name versus Stage Name"

As #nouns, the difference is that name means “any nounal word or phrase which indicates a particular person, place, class, or thing,” but stage name means “the pseudonym of an
I was a recipient of KGSP for my Msc from 2013-2016

Korean Government Scholarship Program (KGSP) Application made easy

Application period- 1 February 2021- 31 March, 2021

MS- 3yrs (1 year Korean language + 2 years MS)

PhD- 4yrs (1 year Korean language + 3 years PhD)

How to navigate the
https://t.co/6Ne99JDfyv page

1. Type https://t.co/ow51lWVKcQ in your browser and hit the enter button

2. Click on scholarships and select GKS notice as attached in the picture👇

3. Play with the notice dashboard to see various announcements from NIIED.


4. E.g in 2020, the Global Korea Scholarship for Graduate Degrees was announced on 11, February as indicated by no 205. You can click to download the application materials to get familiar with what is expected. I attached series of links in this thread to assist too.

Category- All fields

Benefits

1. Visa fee

2. Airfare: Actual cost (To and fro from your home country to Korea and upon completion to your home country)

3. Resettlement Allowance: KRW 200,000 (Given upon arrival in Korea)

4. Monthly stipend: Graduate (MS/PhD)-KRW 1000,000 (362,610.35 Nigerian Naira) per month ,Research Program including Postdoctoral fellow and visiting Professors - 1,500,000 KRW (542,824.78 Nigerian Naira) per month

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This is a pretty valiant attempt to defend the "Feminist Glaciology" article, which says conventional wisdom is wrong, and this is a solid piece of scholarship. I'll beg to differ, because I think Jeffery, here, is confusing scholarship with "saying things that seem right".


The article is, at heart, deeply weird, even essentialist. Here, for example, is the claim that proposing climate engineering is a "man" thing. Also a "man" thing: attempting to get distance from a topic, approaching it in a disinterested fashion.


Also a "man" thing—physical courage. (I guess, not quite: physical courage "co-constitutes" masculinist glaciology along with nationalism and colonialism.)


There's criticism of a New York Times article that talks about glaciology adventures, which makes a similar point.


At the heart of this chunk is the claim that glaciology excludes women because of a narrative of scientific objectivity and physical adventure. This is a strong claim! It's not enough to say, hey, sure, sounds good. Is it true?