ostad-ai / Machine-Learning

This repository contains topics and codes related to Machine Learning and Data Science, especially in Python

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.