- Probability & Statistics
- Linear Algebra
- Applied resources
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 and 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
- Business statistics for contemporary decision making
- Mathematical Statistics with applications
- All of statistics
- 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 😊😃!
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.