├── .DS_Store ├── 01 Linear Regression ├── Gradient_Descent_Demo.ipynb ├── Linear_Regression.ipynb └── Linear_Regression_with_Multiple_Features.ipynb ├── 02 Logistic Regression └── Logistic_Regression.ipynb ├── 03 Dimensionality Reduction └── PCA.ipynb ├── 04 PCA └── PCA.ipynb ├── 05 KNN └── K_Nearest_Neigbours.ipynb ├── 06 K-Means └── K_Means.ipynb ├── 07 Naive Bayes ├── Naive Bayes For Text Classification.ipynb └── Spam Classifier.ipynb ├── 08 Decision Trees ├── Decision Trees.ipynb └── Titanic Survival Prediction.ipynb └── 09 Ensemble Learning ├── Customer_Churn_Prediction.ipynb ├── Ensemble - Bagging.ipynb └── Ensemble Learning - Boosting.ipynb /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/prateek27/machine-learning-essentials/ead6bf8c01764b86b12e1471255bb1e6cf7064b1/.DS_Store -------------------------------------------------------------------------------- /01 Linear Regression/Gradient_Descent_Demo.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [] 7 | }, 8 | "kernelspec": { 9 | "name": "python3", 10 | "display_name": "Python 3" 11 | }, 12 | "language_info": { 13 | "name": "python" 14 | } 15 | }, 16 | "cells": [ 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": { 21 | "id": "PfFSSy_kpLSc" 22 | }, 23 | "outputs": [], 24 | "source": [ 25 | "import numpy as np \n", 26 | "import matplotlib.pyplot as plt" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "source": [ 32 | "# Generate data using a dummy function \n", 33 | "X = np.arange(10)\n", 34 | "Y = (X-5)**2 + 3 \n", 35 | "\n", 36 | "print(X,Y)" 37 | ], 38 | "metadata": { 39 | "colab": { 40 | "base_uri": "https://localhost:8080/" 41 | }, 42 | "id": "cxRCoyRWp31i", 43 | "outputId": "1ac5d03b-7f79-4dd0-89b2-c98b65f1e8a9" 44 | }, 45 | "execution_count": null, 46 | "outputs": [ 47 | { 48 | "output_type": "stream", 49 | "name": "stdout", 50 | "text": [ 51 | "[0 1 2 3 4 5 6 7 8 9] [28 19 12 7 4 3 4 7 12 19]\n" 52 | ] 53 | } 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "source": [ 59 | "# Visualise \n", 60 | "plt.style.use(\"seaborn\")\n", 61 | "plt.plot(X,Y)\n", 62 | "plt.xlabel(\"X\")\n", 63 | "plt.ylabel(\"Y\")\n", 64 | "plt.show()" 65 | ], 66 | "metadata": { 67 | "colab": { 68 | "base_uri": "https://localhost:8080/", 69 | "height": 361 70 | }, 71 | "id": "L4q4bzPkqNno", 72 | "outputId": "da1944a4-4878-4301-8f68-08eeb8990813" 73 | }, 74 | "execution_count": null, 75 | "outputs": [ 76 | { 77 | "output_type": "display_data", 78 | "data": { 79 | "text/plain": [ 80 | "

	docID	words in document	c = China?
training set	1	Chinese Beijing Chinese	yes
	2	Chinese Chinese Shanghai	yes
	3	Chinese Macao	yes
	4	Tokyo Japan Chinese	no
test set	5	Chinese Chinese Chinese Tokyo Japan	?

	docID	words in document	c = China?
training set	1	Chinese Beijing Chinese	yes
	2	Chinese Chinese Shanghai	yes
	3	Chinese Macao	yes
	4	Tokyo Japan Chinese	no
test set	5	Chinese Chinese Chinese Tokyo Japan	?

	Outlook	Temperature	Humidity	Windy	Play
0	sunny	hot	high	False	no
1	sunny	hot	high	True	no
2	overcast	hot	high	False	yes
3	rainy	mild	high	False	yes
4	rainy	cool	normal	False	yes