A collection of Markdown files where I took note to understand the concept of machine learning.
- Imbalance binary classification
- Linear Discriminant Analysis (LDA)
- Logistic regression
- Cross-Validation
- Decision tree
- Boosting
- Bagging
- Support vector machine
- Clustering
- Linear model
- An Introduction to Statistical Learning
- Springer, Gareth James/Daniela Witten/Trevor Hastie/Robert Tibshirani
- "ISL". I used in Columbia University machine learning classes.
- The Elements of Statistical Learning
- Springer, Trevor Hastie/Robert Tibshirani/Jerome Friedman
- "ESL". I used in Columbia University machine learning classes.
- Practical Statistics for Data Scientists
- O'REILLY, Peter Bruce
- A/B testing
- Machine Learning Design Patterns
- O'REILLY, Valliappa Lakshmanan
- Mathematical Methods in the Physical Sciences
- Mary L Boas
- Linear algebra and calculus
- Introduction to Linear Algebra
- Gilbert Strang, Wellesley-Cambridge Press
- Introduction to Linear Alg ebra, Fifth Edition (2016)
- Graduate students textbook
- Deep Learning Specialization
- Understand neural network
- Mathematics for Machine Learning Specialization
- Understand linear algebra
- Machine Learning
- Support vector machine
- By Andrew Ng
- Complete linear algebra: theory and implementation in code
- Principal component analysis
- Least-squares
- Eigendecomposition
- Singular value decomposition
- Master statistics & machine learning: intuition, math, code
- Traditional statistics basics
- Machine Learning Mastery
- 3Blue1Brown
- Series of videos developing mathematical intuition
- StatQuest
- 3Blue1Brown
- Series of videos developing mathematical intuition
- kaggle
- Imbalance class data
- Elements of statistical learning data
- web_page_data.csv
- Web page session time data from O'REILLY Practical Statistics for Data Scientists
| Topic | Title | Link |
|---|---|---|
| XGBoost | XGBoost: A Scalable Tree Boosting System | https://arxiv.org/abs/1603.02754 |
- Review boosting
- Review Gini and Entropy (2021-12-19)
- Review KNN
- Review calibration
- Kaggle credit card fraud detection
- Read ISL 10.3.1 K-Means Clustering
- Read ISL 10.3.2 Hierarchical Clustering
- Read ISL from 9.2 Support Vector Machines
- Read XGBoost paper
- Read SMOTE paper
- Coursera Mathematics for Machine Learning: Linear Algebra
- Didn't understand week 5 calculating eigenvectors
- ESL 3.1 and 3.2
- ESL 4.3 Linear Discriminant Analysis
- Read ESL from 5.5 Automatic Selection of the Smoothing Parameters
- Read ESL from 10.10.3 Implementation of Gradient Boosting
- Read ESL from 11.5.2 Overfitting
- Check AB testing