Machine Learing and Data Science

1. Naive Bayes classifier for categorical data from scratch in Python
2. Naive Bayes classifier for continuous data from scratch in Python
3. Data Visualization: Showing Iris dataset with Blender API
4. Norms in vector space: A review of norms, and reminding p-norms are included. Finally, we compare some special p-norms.
5. Inner products in vector space: Reminding dot product and Frobenius inner product, and then canonical norms based on them. There are examples with module numpy.
6. Gram-Schmidt process: An algorithm to convert a linearly independent set of vectors into an orthogonal set of vectors.
7. Boxplot: The elements of a boxplot are reviewed here, including: medians, quartiles, fences, and outliers.
8. Probability, standard terms: such as sample space, trial, outcome, and event.
9. Logisitic function: It is an S-shaped curve, which is widely used in machine learning and neural networks.
10. Sigmoid functions (curves): Some examples are included. They are widely used in neural networks and deep learning.
11. Conditional probability: We review the conditional probability and based on it, we get the multiplication rule.
12. Inclusion-exclusion principle: We review this principle both in set theory and in probability. Python code is also provided.
13. Probability, independent events: The property of independent events are mentioned here. Also, multiplication rule is included with some examples.
14. Probability, Bayes' rule: The Bayes' rule is expressed here along the total probability theorem. Bayes' rule is defined by conditional probabilities. Some Python code are included too.
15. Linear Regression with Least Squares: When we assume the data points are related through a linear function, we can predict the dependent variable from independent variabe(s). This is a lienar regression. One way to find the parameters of a linear regression is to use a Least Squares estimator. The related Python code clarifies this topic.
16. Ridge Regression with Least Squares: Ridge regression is an extension of linear regression in which a penalty term is included in the loss function. This penalty term is called regularization term. Ridge regression is especially useful when data points are noisy and/or having outliers. It also shows robustness against overfitting.