├── .github
└── book-cover.png
├── .gitignore
├── 1_logistic_regression.ipynb
├── 2_linear_regression.ipynb
├── 3_decision_trees.ipynb
├── 4_k_means.ipynb
├── 5_sentiment_analysis.ipynb
├── 6_recommender_systems.ipynb
├── 7_neural_nets.ipynb
├── 8_reinforcement_learning.ipynb
├── LICENSE
└── README.md
/.github/book-cover.png:
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https://raw.githubusercontent.com/curiousily/Machine-Learning-from-Scratch/60fd9ac4e96da017343792e840b932a8f998d691/.github/book-cover.png
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/.gitignore:
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1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | *.egg-info/
24 | .installed.cfg
25 | *.egg
26 | MANIFEST
27 |
28 | # PyInstaller
29 | # Usually these files are written by a python script from a template
30 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
31 | *.manifest
32 | *.spec
33 |
34 | # Installer logs
35 | pip-log.txt
36 | pip-delete-this-directory.txt
37 |
38 | # Unit test / coverage reports
39 | htmlcov/
40 | .tox/
41 | .coverage
42 | .coverage.*
43 | .cache
44 | nosetests.xml
45 | coverage.xml
46 | *.cover
47 | .hypothesis/
48 | .pytest_cache/
49 |
50 | # Translations
51 | *.mo
52 | *.pot
53 |
54 | # Django stuff:
55 | *.log
56 | local_settings.py
57 | db.sqlite3
58 |
59 | # Flask stuff:
60 | instance/
61 | .webassets-cache
62 |
63 | # Scrapy stuff:
64 | .scrapy
65 |
66 | # Sphinx documentation
67 | docs/_build/
68 |
69 | # PyBuilder
70 | target/
71 |
72 | # Jupyter Notebook
73 | .ipynb_checkpoints
74 |
75 | # pyenv
76 | .python-version
77 |
78 | # celery beat schedule file
79 | celerybeat-schedule
80 |
81 | # SageMath parsed files
82 | *.sage.py
83 |
84 | # Environments
85 | .env
86 | .venv
87 | env/
88 | venv/
89 | ENV/
90 | env.bak/
91 | venv.bak/
92 |
93 | # Spyder project settings
94 | .spyderproject
95 | .spyproject
96 |
97 | # Rope project settings
98 | .ropeproject
99 |
100 | # mkdocs documentation
101 | /site
102 |
103 | # mypy
104 | .mypy_cache/
105 |
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/1_logistic_regression.ipynb:
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1 | {
2 | "nbformat": 4,
3 | "nbformat_minor": 0,
4 | "metadata": {
5 | "colab": {
6 | "name": "1.logistic_regression_final.ipynb",
7 | "version": "0.3.2",
8 | "provenance": [],
9 | "collapsed_sections": []
10 | },
11 | "kernelspec": {
12 | "name": "python3",
13 | "display_name": "Python 3"
14 | },
15 | "accelerator": "GPU"
16 | },
17 | "cells": [
18 | {
19 | "cell_type": "code",
20 | "metadata": {
21 | "id": "GMkVuv4djG4x",
22 | "colab_type": "code",
23 | "colab": {}
24 | },
25 | "source": [
26 | "import numpy as np\n",
27 | "import pandas as pd\n",
28 | "import seaborn as sns\n",
29 | "from pylab import rcParams\n",
30 | "import matplotlib.pyplot as plt\n",
31 | "from collections import OrderedDict\n",
32 | "from scipy.special import expit\n",
33 | "import unittest\n",
34 | "\n",
35 | "%matplotlib inline\n",
36 | "\n",
37 | "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
38 | "\n",
39 | "rcParams['figure.figsize'] = 14, 8\n",
40 | "\n",
41 | "RANDOM_SEED = 42\n",
42 | "\n",
43 | "np.random.seed(RANDOM_SEED)\n",
44 | "\n",
45 | "def run_tests():\n",
46 | " unittest.main(argv=[''], verbosity=1, exit=False)"
47 | ],
48 | "execution_count": 0,
49 | "outputs": []
50 | },
51 | {
52 | "cell_type": "markdown",
53 | "metadata": {
54 | "id": "V-I1L92c1Dno",
55 | "colab_type": "text"
56 | },
57 | "source": [
58 | "# Your data"
59 | ]
60 | },
61 | {
62 | "cell_type": "code",
63 | "metadata": {
64 | "id": "0WFneRgf2NGQ",
65 | "colab_type": "code",
66 | "colab": {}
67 | },
68 | "source": [
69 | "data = OrderedDict(\n",
70 | " amount_spent = [50, 10, 20, 5, 95, 70, 100, 200, 0],\n",
71 | " send_discount = [0, 1, 1, 1, 0, 0, 0, 0, 1]\n",
72 | " )"
73 | ],
74 | "execution_count": 0,
75 | "outputs": []
76 | },
77 | {
78 | "cell_type": "code",
79 | "metadata": {
80 | "id": "Qg_P_LCv6kFZ",
81 | "colab_type": "code",
82 | "outputId": "541955cd-a5c6-4b1f-f974-679e7b39e9d9",
83 | "colab": {
84 | "base_uri": "https://localhost:8080/",
85 | "height": 328
86 | }
87 | },
88 | "source": [
89 | "df = pd.DataFrame.from_dict(data)\n",
90 | "df"
91 | ],
92 | "execution_count": 0,
93 | "outputs": [
94 | {
95 | "output_type": "execute_result",
96 | "data": {
97 | "text/html": [
98 | "\n",
99 | "\n",
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165 | "
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166 | " \n",
167 | "
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168 | "
7 |
8 |
9 | Read the book here
10 |
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