Essential Mathematics for Applied Machine Learning and Data Science
What is this?
A collection of interactive tutorials about essential mathematics for applied machine learning and data science.
How are you doing this?
As a learning resource with the following characteristics:
- Open
- Free
- Interactive (Jupyter Notebooks and blogpost formats)
- Visual
- Python-based
- Math with code, i.e., exemplifying mathematical concepts with Python.
Is this for me?
Maybe. I have no formal mathematical training beyond high school, so I'm writing this from that perspective. This entails the following:
- I use a narrative style, like a conversation with myself or a dear friend who I'm trying to help to understand something.
- I try to explain every bit of notation and to "translate" math expression into natural language.
- I use visual explanations and analogies as much as possible.
- Almost zero proofs.
What are the prerequisites?
This is a tricky question. I'd like to say "NONE! Only your enthusiasm!" but I would be lying. That being said, my best guess is: solid high school math. Now, from where I come from, math education is pretty bad, therefore is unclear what solid means for people from different parts of the world. From my experience writing this I'd say:
- Algebra
- Pre-calculus
- A bit of trigonometry
- Basic notions of probability and sets
What contents do you cover?
- Linear Algebra
- Calculus (Differential, Integral, and Vector Calculus)
- Probability and Statistics
- Information Theory (maybe)
- Optimization (maybe)
Points (4) and (5) are maybes, not because they aren't important, but because my time availability.
How do I use this?
You have three options:
My blog
Read the tutorials in my blog.
Remotely
With mybinder, by clicking the icon ->
This will build a docker image of the repo that runs on the cloud. Beware that it may take 2-3 minutes to be ready. Note the mybinder link may be behind a few updates.
Then navigate to the notebooks directory and access to the tutorial.
Locally
To obtain the files locally, run this in the command line:
git clone https://github.com/pabloinsente/math-app-ml.git
To set up your system, you need python 3.6.x. It is recommended to use a virtual environment before installing the dependencies. To do this, navigate into the cloned repository in the console by:
cd math-app-ml
Note that you may need to change the path to cd into the directory.
Then run this inside that directory to create the virtual environment:
python3 -m venv venv
And activate your environment by running:
source venv/bin/activate
Make sure to have the latest pip version:
pip install --upgrade pip
Install dependencies by running:
pip install -r requirements.txt
To run the notebooks, navigate to the notebooks directory and launch Jupyter Lab as:
jupyter lab
Who are you and why are you doing this?
I'm PhD student in Psychology at UW-Madison. I'm from Santiago, Chile. I'm doing this for a couple of reasons:
- Because teaching (or creating educational content) is the best way to learn for me. I've TA multiple courses over the years, and as a result became convinced the person learns the most after each class is the instructor, not the students.
- Because I like to do things that other people can use, even if is just a small number of individuals. Particularly if those individuals are people like me, that sometimes do not have the money to pay for books or courses.
- Because is a way to show potential employees that I know "things", even math, which is something most people don't believe given my lack of formal training.