├── 1. Linear Regression - Profit vs Population.ipynb
├── 2. Linear Regression - Price vs Size and # of Bedrooms.ipynb
├── 3. Logistic Regression - Admission vs Exam1 and Exam2.ipynb
├── 4. Logistic Regresion - Quality vs Chip test 1 and chip test 2.ipynb
├── 5. SVM Classification.ipynb
├── 6. K-means Clusterization.ipynb
├── 7. OneVsAll - Prediction of Drawn Numbers.ipynb
├── 8. Neural Network - Prediction of Numbers.ipynb
├── Notes Machine Learning in Coursera by AnrewNg.pdf
├── README.md
└── data
├── data1.txt
├── data2.txt
├── data3.txt
├── data4.txt
├── data5a.mat
├── data5b.mat
├── data6.mat
└── data7.mat
/1. Linear Regression - Profit vs Population.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# Profit vs Population\n",
8 | "In this exercise I estimate the profit of a company base on the population of the city."
9 | ]
10 | },
11 | {
12 | "cell_type": "code",
13 | "execution_count": 1,
14 | "metadata": {
15 | "collapsed": false
16 | },
17 | "outputs": [],
18 | "source": [
19 | "import numpy as np\n",
20 | "import pandas as pd\n",
21 | "from scipy.optimize import fmin_cg\n",
22 | "import matplotlib.pyplot as plt\n",
23 | "%matplotlib inline"
24 | ]
25 | },
26 | {
27 | "cell_type": "code",
28 | "execution_count": 2,
29 | "metadata": {
30 | "collapsed": false
31 | },
32 | "outputs": [
33 | {
34 | "data": {
35 | "text/html": [
36 | "\n",
37 | "
\n",
38 | " \n",
39 | " \n",
40 | " | \n",
41 | " Population | \n",
42 | " Profit | \n",
43 | "
\n",
44 | " \n",
45 | " \n",
46 | " \n",
47 | " | 0 | \n",
48 | " 6.1101 | \n",
49 | " 17.5920 | \n",
50 | "
\n",
51 | " \n",
52 | " | 1 | \n",
53 | " 5.5277 | \n",
54 | " 9.1302 | \n",
55 | "
\n",
56 | " \n",
57 | " | 2 | \n",
58 | " 8.5186 | \n",
59 | " 13.6620 | \n",
60 | "
\n",
61 | " \n",
62 | " | 3 | \n",
63 | " 7.0032 | \n",
64 | " 11.8540 | \n",
65 | "
\n",
66 | " \n",
67 | " | 4 | \n",
68 | " 5.8598 | \n",
69 | " 6.8233 | \n",
70 | "
\n",
71 | " \n",
72 | "
\n",
73 | "