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Santiago Profile picture
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12 Apr, 8 tweets, 4 min read
If you are looking to get a background in math before starting with machine learning, here is all the material you need covering the following topics:

• Probabilities & Statistics
• Linear Algebra
• Multivariate Calculus

More than enough to get started.

↓ 1/7
Seeing Theory

seeing-theory.brown.edu

An interactive website that will take you through some of the most critical concepts of Probabilities and Statistics.

These will be enough to get you started, and you will have fun while going through it!

↓ 2/7
Statistics 110: Probability

youtube.com/playlist?list=…

If you are looking for more, this course from Harvard University is an excellent introduction to probability as a language and a set of tools for understanding statistics, science, risk, and randomness.

↓ 3/7
Essence of Linear Algebra

youtube.com/playlist?list=…

Who doesn't love Grant Sanderson's YouTube videos?

Go through this playlist for a refresher in Linear Algebra, and you'll be more than ready to face any machine learning demons.

↓ 4/7
Linear algebra

ocw.mit.edu/courses/mathem…

MIT Course 18.06.

Taught by Profesor Gilbert Strang, this is simply one of the best linear algebra courses that you'll ever find.

Prof. Gilbert makes the subject ridiculously simple and engaging.

↓ 5/7
Mathematics for Machine Learning: Multivariate Calculus

coursera.org/learn/multivar…

A free, beginner-friendly introductory course to building your confidence and introduce you to the multivariate calculus required to build many common machine learning techniques.

↓ 6/7
Essence of Calculus

youtube.com/playlist?list=…

This is Grant Sanderson's excellent take on calculus.

A series of interactive videos that are informative and make calculus feel like something that you could have discovered yourself.

↓ 7/7
If you find these threads helpful, follow me @svpino for a weekly stream of machine learning content.

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More from @svpino

Santiago Profile picture
11 Apr
A lot in machine learning is pretty dry and boring, but understanding how autoencoders work feels different.

This is a thread about autoencoders, things they can do, and a pretty cool example.

↓ 1/10
Autoencoders are lossy data compression algorithms built using neural networks.

A network encodes (compresses) the original input into an intermediate representation, and another network reverses the process to get the same input back.

↓ 2/10
The encoding process "generalizes" the input data.

I like to think of it as the Summarizer in Chief of the network: its entire job is to represent the entire dataset as compactly as possible, so the decoder can do a decent job at reproducing the original data back.

↓ 3/10
Read 12 tweets
Santiago Profile picture
10 Apr
We usually talk about two main types of machine learning models:

• A Classification model
• A Regression model

They are different, and it's essential to understand why.

↓ 1/6
Whenever the result of your predictions is categorical, you have a classification model.

For example, when your prediction is a binary value (True or False,) or when you want to predict a specific animal from a picture (Lion, Zebra, Horse.)

↓ 2/6
If the result of your predictions is numerical, you have a regression model.

For example, returning a stock's future price, the value of a house, or tomorrow's temperature.

↓ 3/6
Read 8 tweets
Santiago Profile picture
10 Apr
No, this is not a joke.

Here are my thoughts about the "HTML is not a programming language" recurrent theme.

↓ 1/5
This question is controversial not because people care about HTML but because it is used as a proxy to classify their worth.

If HTML is not a programming language, then the people working with HTML must not be real programmers, right?

↓ 2/5
This is demeaning and completely unhelpful for those who are starting and looking to find a community.

Instead of drawing lines, we should be welcoming those who want to join us. We need more programmers, coders, developers, or whatever else you want to call them!

↓ 3/5
Read 5 tweets
Santiago Profile picture
9 Apr
About getting a job with machine learning, things you should do during an interview, and some random thoughts that may help you land a job

↓
Before getting a job or making any money, you need to show that you are competent.

Put in the work right now and level up.

There are no hacks, no shortcuts, or magic words that will convince anyone to pay you for something you don't know.

↓
You don't need to get a Ph.D. or a Master's to work as a machine learning practitioner.

I understand many companies are still asking for a degree, but there are many that aren't.

Find and apply to those.

↓
Read 20 tweets
Santiago Profile picture
7 Apr
16 key takeaways about starting a career as a machine learning engineer.

↓ 1/10
1. There are more courses out there than you'll ever need. Pick one and finish it. There's no such thing as "the best course."

2. You probably already know most of the math you'll need to get started.

↓ 2/10
3. Don't be discourage by introductory courses that claim you need a ton of math as a prerequisite. Most don't.

4. You want to be proficient with Python. Other languages are great and all, but Python rules.

↓ 3/10
Read 12 tweets
Santiago Profile picture
7 Apr
Today is #GumroadDay, so let's celebrate with style!

$5 for "How to get started with machine learning" TODAY ONLY!

You can kick off your machine learning career for less than the price of a Starbucks, but this one you can return if you don't like it!

gum.co/kBjbC/crazy
This is working. #GumroadDay is nuts!
Thanks to everyone that has supported me with this course!

Money to feed the family is what affords me the time to post content and focus on helping people with machine learning.

Even if you aren't buying this, like/retweet for visibility!

↓

Read 10 tweets

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