Note Collections

This is a collection of notes that I have compiled on data science/machine learning.


I prepare these primarily for my own reference, but please feel free to use them as well if you find them useful.

Because these are notes that I have prepared by referring to external sources, efforts have been made to always attribute the sources. Please feel free to let me know if any information is not properly attributed.

All Notes

Data Science Career

Collection of notes on data science career topics - everything on how to successfully manage oneself's career in data science field.

Data Science Project Management

Collection of notes on data science project management - everything requires to run a data science project successfully. This includes project workflow and stakeholder management.

Data Exploration and Feature Engineering

Collection of notes on exploratory data analysis and feature selection/engineering - high level overview of key steps in extracting insights out of data before model training.

Model Hyperparameter Tuning

Collection of notes on model hyperparameter tuning - high level overview of techniques used to tune models.

Model Evaluation

Collection of notes on model evaluation - high level overview of techniques used to evaluate model performance.

Linear Models

Collection of notes on model type - focusing on all types of linear models (statistical point of view).

Bayesian Framework

Collection of notes on model type - focusing on Bayesian framework (statistical point of view).

Traditional Machine Learning Models

Collection of notes on model type - focusing on traditional machine learning models.

Time Series Analysis

Collection of notes on time series analysis (statistical point of view).

Linear Algebra Primer

Collection of notes on linear algebra - covers core concepts of linear algebra.

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