# Statistics and Linear Algebra for ML/DL

Diving deep into the statistical underpinings and linear algebraic foundations of learning

Hey there! 👋😃

Today, I am gonna talk about something I have been meaning to do for the longest amout of time. I am now many months into my ML journey, so I have now quite a bit of experience. So, I have a basic understanding of the ML/DL algos but it always feels like I lack in explaining why something works over the others. This is where the mathematical part comes in. Having a sound understanding of the statistical methods is necessary when the explainability comes in. Inorder to do that I have decided to start a *Probability & Statistics* and *Linear Algebra* series. This way I would not only be able to learn both but also share with the community which gave me so much. Also, it’s a part of the *“Feynmann technique”* which I am so fond of.

# Probability & Statistics

## Books:

*Business statistics for contemporary decision making**Mathematical Statistics with applications**All of statistics*

## Video lectures:

# Linear Algebra

## Books:

## Video lectures:

# Applied resources

- My favorite:
*An Introduction to Statistical Learning: With Applications in R* *Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python**Think Stats(Learn stats using Python)**Introduction to Linear Algebra for Applied Machine Learning with Python*- Peter Norvig’s:
*A Concrete Introduction to Probability (using Python)*

As I read the material and understand a concept I will write an article about it. It will be two parellel series one for each. If you want to follow it then all of it will be posted on my **Medium**. I will see you there!

As always, thank you for reading 😊😃!

# Update(21/07/2020)

I need to strike a balance between theory and application. I actually read few chapters of the book but found that I am more into the *code-first* approach to learning. So, there’s a slight modification to my plan. For linear algebra, I am using *Computational linear algebra by Rachel Thomas*. I have just begun and I already like it. I will use it parellely with Glibert Strang’s linear algebra course.

For proabability & stats: *Think Stats* + any theory course mentioned.