├── requirements.txt ├── flashcards ├── C.jpg ├── CP.jpg ├── AIC.jpg ├── Big O.jpg ├── ELUs.jpg ├── KNN.jpg ├── Odds.jpg ├── RSS.jpg ├── Bagging.jpg ├── L1 Norm.jpg ├── Recall.jpg ├── T-Tests.jpg ├── Z-score.jpg ├── tf-idf.jpg ├── Accuracy.jpg ├── AdaBoost.jpg ├── Bootstrap.jpg ├── ElasticNet.jpg ├── F1 Score.jpg ├── F1-Score.jpg ├── Gini Index.jpg ├── Hinge Loss.jpg ├── Hyperplane.jpg ├── Kernel PCA.jpg ├── Lazy KNN.jpg ├── MSE vs MAE.jpg ├── Matrices.jpg ├── Max Norm.jpg ├── Notation 1.jpg ├── Outliars.jpg ├── Power Rule.jpg ├── Precision.jpg ├── R-Squared.jpg ├── Saturation.jpg ├── Sparsity.jpg ├── Stop Words.jpg ├── Underflow.jpg ├── Variance.jpg ├── Brier Score.jpg ├── Chi-Squared.jpg ├── Conditioning.jpg ├── Correlation.jpg ├── Downsampling.jpg ├── Eigenvector.jpg ├── Error Types.jpg ├── F-Statistic.jpg ├── Hamming Loss.jpg ├── Hidden Layer.jpg ├── Kernel Trick.jpg ├── Log-Sum-Exp.jpg ├── Natural Log.jpg ├── Sensitivity.jpg ├── Square Root.jpg ├── Tomek Links.jpg ├── t-statistic.jpg ├── Bayes Theorem.jpg ├── Calculating VIF.jpg ├── Cross-Entropy.jpg ├── Current status..jpg ├── Euclidean Norm.jpg ├── Fowlkes-Mallows.jpg ├── Greek Letters 1.jpg ├── Greek Letters 3.jpg ├── Hessian Matrix.jpg ├── Interpolation.jpg ├── Jacobian Matrix.jpg ├── Learning Curve.jpg ├── Matrix Inverse.jpg ├── Min-Max Scaling.jpg ├── MinMax Scaling.jpg ├── Normalization.jpg ├── Standard Error.jpg ├── Standardization.jpg ├── The Perceptron.jpg ├── Zero-One Loss.jpg ├── Avoid Overfitting.jpg ├── Confusion Matrix.jpg ├── Confusion Matrix2.jpg ├── Decision Boundary.jpg ├── Gradient Descent.jpg ├── Handling Outliers.jpg ├── Interaction Term.jpg ├── Missing At Random.jpg ├── Model Complexity.jpg ├── Multicolinearity.jpg ├── One-Hot Encoding.jpg ├── Out-Of-Bag Error.jpg ├── Ridge Regression.jpg ├── Adjusted R-Squared.jpg ├── Dataset Augmentation.jpg ├── Euclidean Distance.jpg ├── False Positive Rate.jpg ├── Heteroskedasticity.jpg ├── Imputation Using kNN.jpg ├── Linear Combination.jpg ├── Manhattan Distance.jpg ├── Mean Squared Error.jpg ├── Overfit vs Underfit.jpg ├── Parametric Methods.jpg ├── Pearsons Correlation.jpg ├── Perceptron Learning.jpg ├── Principal Components.jpg ├── Threshold Activation.jpg ├── Training Error Rate.jpg ├── Youden's J Statistic.jpg ├── k Nearest Neighbor.jpg ├── Bias-Variance Tradeoff.jpg ├── Convex Hulls with SVMs.jpg ├── Fowlkes-Mallows Score.jpg ├── General Additive Model.jpg ├── Imputation Using k-NN.jpg ├── Missing Not At Random.jpg ├── Naive Bayes Classifier.jpg ├── Non-Parametric Methods.jpg ├── Notions Of Probability.jpg ├── Polynomial Regression.jpg ├── Softmax Normalization.jpg ├── k-NN Neighborhood Size.jpg ├── Aggomerative Clustering.jpg ├── Alpha In Ridge Regression.jpg ├── Conditional Probability.jpg ├── Cost Loss Error Function.jpg ├── Curse Of Dimensionality.jpg ├── Decision Tree Regression.jpg ├── Explained Sum Of Squares.jpg ├── How Norm Penalties Work.jpg ├── How to avoid overfitting.jpg ├── Imputing Missing Values.jpg ├── Motivation for Kernal PCA.jpg ├── Residual Sum Of Squares.jpg ├── Training Error Rate copy.jpg ├── Variance Inflation Factor.jpg ├── k-Fold Cross-Validation.jpg ├── Backward Stepwise Selection.jpg ├── Correlation of Error Terms.jpg ├── Covariance and Correlation.jpg ├── Effect of K's Value in KNN.jpg ├── Exploding Gradient Problem.jpg ├── Precision-Recall Trade-off.jpg ├── Stocastic Gradient Descent.jpg ├── Supervised Vs Unsupervised.jpg ├── The Random In Random Forest.jpg ├── Vanishing Gradient Problem.jpg ├── Basic Parts Of Deep Learning.jpg ├── Common Mathematical Notation.jpg ├── Concave And Convex Functions.jpg ├── Cost Loss And Error Functions.jpg ├── Data-Generating Distribution.jpg ├── Feature Selection Strategies.jpg ├── Leave One Out Cross Validation.jpg ├── Linear Discriminant Analysis.jpg ├── Parameters vs Hyperparameters.jpg ├── Principal Component Analysis.jpg ├── Probability Density Function.jpg ├── Random Forest Classification.jpg ├── Singular Value Decomposition.jpg ├── Therefore And Because Notation.jpg ├── Unit-Step Activation Function.jpg ├── Variance For Feature Selection.jpg ├── What are principal components.jpg ├── Cumulative Distribution Function.jpg ├── Fundamental Theorem of Calculus.jpg ├── LDA for Dimensionality Reduction.jpg ├── Meanshift Clustering By Analogy.jpg ├── Pearsons Correlation Coefficient.jpg ├── Selecting # of components in PCA.jpg ├── Chi-Squared for Feature Selection.jpg ├── Coefficient of Determination (R2).jpg ├── K-Nearest Neighbors Tips And Tricks.jpg ├── Slack Variable In Soft-Margin SVM.jpg ├── Supervised Vs Unsupervised Learning.jpg ├── The Argument For Parametric Models.jpg ├── Finding Linear Regression Parameters.jpg ├── Preprocessing Test And Training Sets.jpg ├── Preprocessing When Tests and Training.jpg ├── The Effect Of Dropout On Hidden Units.jpg ├── Common Optimizers With Neural Networks.jpg ├── Common Output Layer Activation Functions.jpg ├── Linear Regression Problems and 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-------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Extracting images from the #machinelearningflashcards twitter hashtag 2 | A very basic Python script to pull machine learning flashcards from Chris Albon's #machinelearningflashcards hashtag 3 | 4 | All work on the flashcards are the work of Chris Albon (@chrisalbon). 5 | 6 | Project was inspired by [Jasmine Dumas'](http://jasdumas.github.io/) [project](https://github.com/jasdumas/ml-flashcards) in R. 7 | 8 | ### Instructions 9 | In order to access the Twitter API you need a Consumer Key and Consumer Secret Key. You can obtain these by using your Twitter account to create a twitter app at https://apps.twitter.com/app/new. Proceed to fill out the required information. 10 | 11 | Once completed you'll be taken to a page containing the Consumer Key and Consumer Secret Key. Place these keys in a file called twitter_keys.py in the main directory. The python file you create should have two variables `consumer_key` and `consumer_secret` which should contain the keys you just obtained from twitter. The file should look something like this, where you replace the given text with the keys you obtained. 12 | 13 | ```python 14 | # Variables that contains the user credentials to access Twitter API 15 | consumer_key = "ENTER CONSUMER KEY HERE" 16 | consumer_secret = "ENTER CONSUMER SECRET KEY HERE" 17 | ``` 18 | 19 | After that you should be able to run the script and add any new flashcards created to the flashcards folder. 20 | 21 | _Note that the twitter search API as used in this script does not include ALL tweets, only recent tweets. I have included previous tweets manually to have them all in one place however the script will only retrieve new tweets._ 22 | 23 | 24 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # key files 2 | twitter_keys.py 3 | .idea/ 4 | 5 | # Byte-compiled / optimized / DLL files 6 | __pycache__/ 7 | *.py[cod] 8 | *$py.class 9 | 10 | # C extensions 11 | *.so 12 | 13 | # Distribution / packaging 14 | .Python 15 | env/ 16 | build/ 17 | develop-eggs/ 18 | dist/ 19 | downloads/ 20 | eggs/ 21 | .eggs/ 22 | lib/ 23 | lib64/ 24 | parts/ 25 | sdist/ 26 | var/ 27 | wheels/ 28 | *.egg-info/ 29 | .installed.cfg 30 | *.egg 31 | 32 | # PyInstaller 33 | # Usually these files are written by a python script from a template 34 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 35 | *.manifest 36 | *.spec 37 | 38 | # Installer logs 39 | pip-log.txt 40 | pip-delete-this-directory.txt 41 | 42 | # Unit test / coverage reports 43 | htmlcov/ 44 | .tox/ 45 | .coverage 46 | .coverage.* 47 | .cache 48 | nosetests.xml 49 | coverage.xml 50 | *.cover 51 | .hypothesis/ 52 | 53 | # Translations 54 | *.mo 55 | *.pot 56 | 57 | # Django stuff: 58 | *.log 59 | local_settings.py 60 | 61 | # Flask stuff: 62 | instance/ 63 | .webassets-cache 64 | 65 | # Scrapy stuff: 66 | .scrapy 67 | 68 | # Sphinx documentation 69 | docs/_build/ 70 | 71 | # PyBuilder 72 | target/ 73 | 74 | # Jupyter Notebook 75 | .ipynb_checkpoints 76 | 77 | # pyenv 78 | .python-version 79 | 80 | # celery beat schedule file 81 | celerybeat-schedule 82 | 83 | # SageMath parsed files 84 | *.sage.py 85 | 86 | # dotenv 87 | .env 88 | 89 | # virtualenv 90 | .venv 91 | venv/ 92 | ENV/ 93 | 94 | # Spyder project settings 95 | .spyderproject 96 | .spyproject 97 | 98 | # Rope project settings 99 | .ropeproject 100 | 101 | # mkdocs documentation 102 | /site 103 | 104 | # mypy 105 | .mypy_cache/ 106 | -------------------------------------------------------------------------------- /ml_flashcard_script_basic.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | import requests 4 | import tweepy 5 | import re 6 | import os 7 | 8 | # create a file twitter_keys.py and add the tokens/keys below as variables 9 | # in the file 10 | from twitter_keys import consumer_key, consumer_secret 11 | 12 | # Create the OAuthHandler 13 | auth = tweepy.OAuthHandler(consumer_key=consumer_key, consumer_secret=consumer_secret) 14 | # Construct the API instance 15 | api = tweepy.API(auth) 16 | 17 | params = { 18 | 'q': 'machinelearningflashcards.com-filter:retweets', 19 | 'from': 'chrisalbon', 20 | 'since': '2018-04-01', 21 | } 22 | 23 | ml_flashcards_json = api.search(**params) 24 | 25 | media_urls = [] 26 | titles = [] 27 | error_counter = 0 28 | total_tweets = len(ml_flashcards_json) 29 | successful_tweets = total_tweets - error_counter 30 | 31 | for i in range(total_tweets): 32 | txt = ml_flashcards_json[i] 33 | json = txt._json 34 | title = re.sub("#\S*", "", str(json['text'])) # removes hashtag 35 | title = re.sub("https\S*", "", title) # removes url 36 | title = str(title.strip()) 37 | try: # KeyError is raised when there is no image within the tweet 38 | media_url = json['entities']['media'][0]['media_url'] 39 | media_urls.append(media_url) 40 | titles.append(title) 41 | except KeyError: 42 | error_counter += 1 43 | 44 | if error_counter == 0: 45 | print("{} tweets were processed successfully".format(successful_tweets)) 46 | elif error_counter == 1: 47 | print("{} tweets were processed successfully".format(successful_tweets)) 48 | print("{} tweet was not processed due to a KeyError".format(error_counter)) 49 | else: 50 | print("{} tweets were processed successfully".format(successful_tweets)) 51 | print("{} tweets were not processed due to a KeyError".format(error_counter)) 52 | 53 | # change directory to flashcards folder 54 | current_dirctory = os.getcwd() 55 | os.chdir(current_dirctory + "/flashcards") 56 | 57 | # write images to the flashcards directory 58 | for i in zip(media_urls, titles): 59 | img = requests.get(i[0]) 60 | f = open(str(i[1]) + ".jpg", mode='wb') 61 | f.write(img.content) 62 | f.close() 63 | -------------------------------------------------------------------------------- /python-ml-flashcards-basic.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import requests\n", 12 | "import tweepy\n", 13 | "import re\n", 14 | "import os\n", 15 | "\n", 16 | "# create a file twitter_keys.py and add the tokens/keys below as variables in the file\n", 17 | "from twitter_keys import consumer_key, consumer_secret" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 3, 23 | "metadata": { 24 | "collapsed": true 25 | }, 26 | "outputs": [], 27 | "source": [ 28 | "# Create the OAuthHandler\n", 29 | "auth = tweepy.OAuthHandler(consumer_key=consumer_key,\n", 30 | " consumer_secret=consumer_secret)\n", 31 | "# Construct the API instance\n", 32 | "api = tweepy.API(auth)\n", 33 | "\n", 34 | "# query parameter excludes the intentional photo to dirty the data\n", 35 | "params = {'q': '-\"are%20-being%20-made\"%20-\"your%20-data%20-dirty\"%20%23machinelearningflashcards-filter:retweets',\n", 36 | " 'from': 'chrisalbon',\n", 37 | " 'since': '2017-04-01', }" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 4, 43 | "metadata": { 44 | "collapsed": true 45 | }, 46 | "outputs": [], 47 | "source": [ 48 | "# query the API\n", 49 | "ml_flashcards_json = api.search(**params)" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 6, 55 | "metadata": {}, 56 | "outputs": [ 57 | { 58 | "name": "stdout", 59 | "output_type": "stream", 60 | "text": [ 61 | "1 tweet was not processed due to a KeyError\n" 62 | ] 63 | } 64 | ], 65 | "source": [ 66 | "# Create two lists: A list of image URLs and Titles\n", 67 | "media_urls = []\n", 68 | "titles = []\n", 69 | "for i in range(len(ml_flashcards_json)):\n", 70 | " txt = ml_flashcards_json[i]\n", 71 | " json = txt._json\n", 72 | " title = re.sub(\"#\\S*\", \"\", str(json['text'])) # removes hashtag\n", 73 | " title = re.sub(\"http\\S*\", \"\", title) # removes url\n", 74 | " title = str(title.strip()) # strips spacing away\n", 75 | " try: # KeyError is raised when there is no image attached to the tweet\n", 76 | " media_url = json['entities']['media'][0]['media_url']\n", 77 | " media_urls.append(media_url)\n", 78 | " titles.append(title)\n", 79 | " except KeyError:\n", 80 | " counter = 0\n", 81 | " counter += 1\n", 82 | " print(\"{} tweet was not processed due to a KeyError\".format(counter))" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 39, 88 | "metadata": { 89 | "collapsed": true 90 | }, 91 | "outputs": [], 92 | "source": [ 93 | "# output the images to the flashcards directory\n", 94 | "current_dirctory = os.getcwd()\n", 95 | "os.chdir(current_dirctory + \"/flashcards\")\n", 96 | "for i in zip(media_urls, titles):\n", 97 | " img = requests.get(i[0])\n", 98 | " f = open(str(i[1]) + \".jpg\", mode='wb')\n", 99 | " f.write(img.content)\n", 100 | " f.close()" 101 | ] 102 | } 103 | ], 104 | "metadata": { 105 | "kernelspec": { 106 | "display_name": "Python 3", 107 | "language": "python", 108 | "name": "python3" 109 | }, 110 | "language_info": { 111 | "codemirror_mode": { 112 | "name": "ipython", 113 | "version": 3 114 | }, 115 | "file_extension": ".py", 116 | "mimetype": "text/x-python", 117 | "name": "python", 118 | "nbconvert_exporter": "python", 119 | "pygments_lexer": "ipython3", 120 | "version": "3.6.1" 121 | } 122 | }, 123 | "nbformat": 4, 124 | "nbformat_minor": 2 125 | } 126 | --------------------------------------------------------------------------------