{"repo":"ostad-ai/Machine-Learning","free":true,"listed":false,"github":"https://github.com/ostad-ai/Machine-Learning","clone":"git clone https://github.com/ostad-ai/Machine-Learning.git","description":"This repository contains topics and codes related to Machine Learning and Data Science, especially in Python","language":"Jupyter Notebook","stars":32,"topics":["data-science","data-visualization","machine-learning","naive-bayes","python","gram-schmidt","linear-regression","ridge-regression","gradient-descent","elastic-net"],"license":null,"category":"machine-learning","readme_excerpt":"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' r","default_branch":null,"files":null,"tree":[],"storefront":"/r/ostad-ai","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ostad-ai/Machine-Learning/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}