{
"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
}