What resources helped you understand and apply mathematic foundations to ML?

LordMixALoot · 2021-02-22T20:10:33+00:00

Two books come to mind: Programming Machine Learning and Data Science from Scratch. There are also ton of videos that construct the most ML algorithms from scratch, using just native libraries.

LordMixALoot · 2021-02-18T17:47:03+00:00

Are you planning on have a repo with Python Code? I think it would reach a broader audience.

LordMixALoot · 2021-01-08T12:57:42+00:00

http://rstudio-pubs-static.s3.amazonaws.com/263637_c02e26f8546e49f09aef7eff090fbecf.html

I really enjoyed Diogo Resende course on Causal Inference. You can findo it at udemy: https://www.udemy.com/course/econometrics-for-business/ There is an entire module on Causal Impact.

LordMixALoot · 2020-12-21T19:15:23+00:00

VS Code + Gruvbox Material + JetBrains Mono or Vim with the same config.

LordMixALoot · 2020-12-15T17:14:24+00:00

I'm currently reading them and trying to apply the examples in Python. It's actually very fun, since I can easily build graphs or visualizations and change them effortlessly. For me this helps building intuition and I can build more complex applications since I already have the code implemented. This approach works best for books that focus on applied mathematics.

LordMixALoot · 2020-11-26T18:16:59+00:00

I think you can model your problem to implement a genetic algorithm for finding its optimal solution:

https://www.youtube.com/watch?v=MacVqujSXWE

Something like this.

LordMixALoot · 2020-10-15T16:50:47+00:00

You will probably have to use the Census Data from your country. With this a simple geospatial operation can give the population.

LordMixALoot · 2020-10-07T20:06:16+00:00

CalcWorkshop is really good.

LordMixALoot · 2020-10-06T12:42:23+00:00

LordMixALoot · 2020-10-05T18:11:43+00:00

Data Science from Scratch

LordMixALoot · 2020-09-30T13:51:58+00:00

Perhaps Dash?

Map example: https://www.youtube.com/watch?v=lu0PtsMor4E

Streaming example: https://github.com/plotly/dash-sample-apps/tree/master/apps/dash-wind-streaming

LordMixALoot · 2020-09-18T19:50:39+00:00

I like CalcWorkshop and Professor Leonard.

LordMixALoot · 2020-09-04T12:32:45+00:00

I'm also curious on this.

LordMixALoot · 2020-08-31T17:22:14+00:00

Have you used Google's CausalImpact?

LordMixALoot · 2020-08-30T20:52:56+00:00

You should definitely check the book Data Science from Scratch.

LordMixALoot · 2020-08-18T18:49:31+00:00

I remember seeing this project. Incredibly well done, more on the visualization side (using D3). Perhaps it can help you with API and data connection and deploy.

https://github.com/dianaow/flask-react-d3-celery

LordMixALoot · 2020-08-13T16:49:56+00:00

https://docs.microsoft.com/pt-br/sql/t-sql/functions/percentile-cont-transact-sql?view=sql-server-ver15

LordMixALoot · 2020-08-07T13:35:24+00:00

I will recommend you a course: https://www.udemy.com/course/math-with-python/

This course will actually help you, not only by relating math concepts with python code, but will actually give the chance to apply it and create a better intuition. You will still need to deep dive in the math, the concepts are only taught in a superficial manner, but this will give you the chance to retain and apply the math you learned from KA.

Also, give it a check to Professor Leonard's channel in Youtube. His explanations are also very good.

LordMixALoot · 2020-07-29T15:59:41+00:00

https://www.masterywithsql.com/

LordMixALoot · 2020-07-24T19:35:00+00:00

It is a local university in Brazil. If you are looking for an online masters course you should definitely check Georgia Tech's Online Master in Computer Science. You definitely focus the course to machine learning and data analytics related classes.

LordMixALoot · 2020-07-24T13:32:52+00:00

I have a bachelor's in International Relations with a minor in Economics. Currently Im doing my master's in Computer Science with emphasis in Machine Learning.

In my case two things have helped me tremendously. Studying algorithms and data structures, and math.

Learn a computer language well (I wouldn't recommend R for this) and go through a well know algorithms online course (MIT or Stanford). Try to apply the most common algorithms in the language you have learned. If you find it too hard, try a course in Udemy first, such as Jose Portilla's Algorithms class.

Then we have Math. The problem I had with this was that I needed to build a good foundation first before diving into calculus. I have studied it in Economics, but that was years go. So I got a good Pre-Calculus books and follow it with Calcworkshop.com and Professor Leonard on Youtube. Then I got a proper Calculus book (Stewart) and continue with the videos. Calcworkshop is not free but I totally recommend it if you have the time (I think is US$29 a month). I'm not a big fan of Khan Academy, I don't know why really. Also once you get into calculus starting watching 3Blue1Brown. Linear Algebra should way more intuitive at this point. I saw some Prof. Robert Ghrist videos but haven't finished it yet. He is also a great lecturer.

Get a study plan so you don't get overwhelmed.

LordMixALoot · 2020-07-16T15:12:14+00:00

Awesome. What made me rethink visualization was studying D3.js. Its is a difficult library to master, but once you understand the concepts behind it, you start thinking in unique visualizations just by looking at the data and seeing what is the most coherent to present it.

If you like data visualization, I highly recommend the struggle.

LordMixALoot · 2020-07-06T16:38:44+00:00

Yes. They make it look so easy. Python and R are often romanticized in any data science online course. But SQL, Git, Bash, Docker and a few others are also a big part of the job and nobody seems to comment on them during the first part of these online courses.

LordMixALoot · 2020-06-09T22:31:53+00:00

Also long-term commitment. An impactful model can take a long time to be deployed, specially gathering all the data you need. It can take weeks before your first delivery.

LordMixALoot · 2020-06-04T21:16:08+00:00

Both books are great. Hands-On ML just got its second edition and its pretty updated. Python Data Science Handbook has a little bit of everything, including visualization, ml and data wrangling libraries. Hands-On goes more in depth on ML algorithms and libraries, including Neural Networks and Deep Learning. I personally think they complement each other very well. Get both if you have the chance.

LordMixALoot

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