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Notes and codes on AI/ML topics
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{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# AI and Machine Learning algorithm notes\n", "\n", "### Supervised algorithms\n", "\n", "\n", "\n", "|Sr.No.| Algorithm | Classification | Regression | Notes |\n", "|------|:--------------------------------------|:--------------:|:----------:|------------------|\n", "|1 |Naive Bayes | Yes | No | - |\n", "\n", "\n", "\n", "\n", "1. [Naive Bayes (Using Gaussian NB) for Classification](ml_naive_bayes.ipynb)\n", "2. Support Vector Machines (SVM) for Classification\n", "3. Decision Tree for Classification and Regression\n", "4. K Nearest Neighbours for Classification and Regression\n", "5. AdaBoost for Classification (in conjunction with Decision Tree)\n", "6. Random Forest\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unsupervised Algorithms\n", "\n", "Used to learn clusters of probability distribution of data\n", "\n", "### Clustering\n", "\n", "1. K Means Clustering\n", "2. Hierarchial Clustering\n", "3. Gaussian Mixture Models, Probability Density Estimation (Under certain conditions, this is same as K Means)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.5" } }, "nbformat": 4, "nbformat_minor": 2 }
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