Word Cloud for a Website

A Wordcloud (or Tag cloud) is a visual representation of text data. It displays a list of words, the importance of each beeing shown with font size or color.

In this document, we are going to learn

1. Web scraping
2. Use of regular expression to clean text
3. NLTK Tokenization
4. Use of STOP WORDS
5. Get the word frequencies
6. Create WordCloud
7. Plot WordCloud with the help of Matplotlib

Extract data from a website

Import modules

from bs4 import BeautifulSoup
from urllib.request import Request, urlopen
import re

Web scraping

The urllib.request module defines functions and classes which help in opening URLs (mostly HTTP) in a complex world — basic and digest authentication, redirections, cookies and more.

urllib.request.urlopen(url, data=None, [timeout, ]*, cafile=None, capath=None, cadefault=False, context=None): Open the URL url, which can be either a string or a Request object.

url = "https://en.wikipedia.org/wiki/Machine_learning"
headers = {'User-Agent': 'Mozilla/5.0'}

req = Request(url, headers = headers )
page = urlopen(req)
page

<http.client.HTTPResponse at 0x7f2dc0238310>

Scrape information from web page

Beautiful Soup is a library that makes it easy to scrape information from web pages.

soup = BeautifulSoup(page, 'html.parser')
print(soup.get_text().encode("utf-8")[:100])

b'\n\n\n\nMachine learning - Wikipedia\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMachine learning\n\nFrom Wik'

Remove script and style elements from scraped information

# kill all script and style elements
for script in soup(["script", "style"]):
    script.extract()    

Get text of web page

text = soup.get_text().strip()
print(text[:100])

Machine learning - Wikipedia









































Machine learning

From Wikiped

Clean text

Remove all spaces from left and right and then make all words lower

#remove extra space from left and right and make it lower
text = text.strip()
text = text.lower()
text[:100]

'machine learning - wikipedia\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nmachine learning\n\nfrom wikiped'

Clean text with the help of regular expression

#\w: returns the alpha numeric words
text = re.sub(r'[^\w]+',' ', text)

#remove digits
text = re.sub('\d+', ' ', text)
print(text[:1000])

machine learning wikipedia machine learning from wikipedia the free encyclopedia jump to navigation jump to search study of algorithms that improve automatically through experience for the journal see machine learning journal statistical learning redirects here for statistical learning in linguistics see statistical learning in language acquisition part of a series onmachine learningand data mining problems classification clustering regression anomaly detection data cleaning automl association rules reinforcement learning structured prediction feature engineering feature learning online learning semi supervised learning unsupervised learning learning to rank grammar induction supervised learning classification regression decision trees ensembles bagging boosting random forest k nn linear regression naive bayes artificial neural networks logistic regression perceptron relevance vector machine rvm support vector machine svm clustering birch cure hierarchical k means expectation maximizat

Word Tokenize

Tokenization is the process of converting a sequence of characters into a sequence of tokens.

The tok-tok tokenizer is a simple, general tokenizer, where the input has one sentence per line; thus only final period is tokenized.

import collections
from nltk.tokenize.toktok import ToktokTokenizer
from nltk.corpus import stopwords

#Tokenizers divide strings into lists of substrings.
tokenizer = ToktokTokenizer()
tokens = tokenizer.tokenize(text)
print(tokens)

['machine', 'learning', 'wikipedia', 'machine', 'learning', 'from', 'wikipedia', 'the', 'free', 'encyclopedia', 'jump', 'to', 'navigation', 'jump', 'to', 'search', 'study', 'of', 'algorithms', 'that', 'improve', 'automatically', 'through', 'experience', 'for', 'the', 'journal', 'see', 'machine', 'learning', 'journal', 'statistical', 'learning', 'redirects', 'here', 'for', 'statistical', 'learning', 'in', 'linguistics', 'see', 'statistical', 'learning', 'in', 'language', 'acquisition', 'part', 'of', 'a', 'series', 'onmachine', 'learningand', 'data', 'mining', 'problems', 'classification', 'clustering', 'regression', 'anomaly', 'detection', 'data', 'cleaning', 'automl', 'association', 'rules', 'reinforcement', 'learning', 'structured', 'prediction', 'feature', 'engineering', 'feature', 'learning', 'online', 'learning', 'semi', 'supervised', 'learning', 'unsupervised', 'learning', 'learning', 'to', 'rank', 'grammar', 'induction', 'supervised', 'learning', 'classification', 'regression', 'decision', 'trees', 'ensembles', 'bagging', 'boosting', 'random', 'forest', 'k', 'nn', 'linear', 'regression', 'naive', 'bayes', 'artificial', 'neural', 'networks', 'logistic', 'regression', 'perceptron', 'relevance', 'vector', 'machine', 'rvm', 'support', 'vector', 'machine', 'svm', 'clustering', 'birch', 'cure', 'hierarchical', 'k', 'means', 'expectation', 'maximization', 'em', 'dbscan', 'optics', 'mean', 'shift', 'dimensionality', 'reduction', 'factor', 'analysis', 'cca', 'ica', 'lda', 'nmf', 'pca', 'pgd', 't', 'sne', 'structured', 'prediction', 'graphical', 'models', 'bayes', 'net', 'conditional', 'random', 'field', 'hidden', 'markov', 'anomaly', 'detection', 'k', 'nn', 'local', 'outlier', 'factor', 'artificial', 'neural', 'network', 'autoencoder', 'cognitive', 'computing', 'deep', 'learning', 'deepdream', 'multilayer', 'perceptron', 'rnn', 'lstm', 'gru', 'esn', 'restricted', 'boltzmann', 'machine', 'gan', 'som', 'convolutional', 'neural', 'network', 'u', 'net', 'transformer', 'vision', 'spiking', 'neural', 'network', 'memtransistor', 'electrochemical', 'ram', 'ecram', 'reinforcement', 'learning', 'q', 'learning', 'sarsa', 'temporal', 'difference', 'td', 'theory', 'kernel', 'machines', 'bias', 'variance', 'tradeoff', 'computational', 'learning', 'theory', 'empirical', 'risk', 'minimization', 'occam', 'learning', 'pac', 'learning', 'statistical', 'learning', 'vc', 'theory', 'machine', 'learning', 'venues', 'neurips', 'icml', 'ml', 'jmlr', 'arxiv', 'cs', 'lg', 'related', 'articles', 'glossary', 'of', 'artificial', 'intelligence', 'list', 'of', 'datasets', 'for', 'machine', 'learning', 'research', 'outline', 'of', 'machine', 'learning', 'vte', 'part', 'of', 'a', 'series', 'onartificial', 'intelligence', 'major', 'goals', 'artificial', 'general', 'intelligence', 'planning', 'computer', 'vision', 'general', 'game', 'playing', 'knowledge', 'reasoning', 'machine', 'learning', 'natural', 'language', 'processing', 'robotics', 'approaches', 'symbolic', 'deep', 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Remove common words

Stop words are words which are filtered out before or after processing of natural language data (text). Stop words usually refers to the most common words in a language, there is no single universal list of stop words used by all natural language processing tools.

tokens = [word for word in tokens if not word in stopwords.words()]
tokens

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 'programs',
 'perform',
 'tasks',
 'without',
 'explicitly',
 'programmed',
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 'computers',
 'learning',
 'data',
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 'simple',
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 'every',
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 'various',
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 'teach',
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 'cases',
 'vast',
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 'exist',
 'approach',
 'label',
 'correct',
 'answers',
 'valid',
 'used',
 'training',
 'data',
 'computer',
 'improve',
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 'uses',
 'determine',
 'correct',
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 'example',
 'train',
 'system',
 'task',
 'digital',
 'character',
 'recognition',
 'mnist',
 'dataset',
 'handwritten',
 'digits',
 'often',
 'used',
 'history',
 'relationships',
 'fields',
 'edit',
 'see',
 'timeline',
 'machine',
 'learning',
 'term',
 'machine',
 'learning',
 'coined',
 'arthur',
 'samuel',
 'american',
 'ibmer',
 'pioneer',
 'field',
 'computer',
 'gaming',
 'artificial',
 'intelligence',
 'synonym',
 'self',
 'teaching',
 'computers',
 'used',
 'time',
 'period',
 'representative',
 'book',
 'machine',
 'learning',
 'research',
 'nilsson',
 'book',
 'learning',
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 'dealing',
 'mostly',
 'machine',
 'learning',
 'pattern',
 'classification',
 'interest',
 'related',
 'pattern',
 'recognition',
 'continued',
 'described',
 'duda',
 'hart',
 'report',
 'given',
 'using',
 'teaching',
 'strategies',
 'neural',
 'network',
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 'recognize',
 'characters',
 'letters',
 'digits',
 'special',
 'symbols',
 'computer',
 'terminal',
 'tom',
 'mitchell',
 'provided',
 'widely',
 'quoted',
 'formal',
 'definition',
 'algorithms',
 'studied',
 'machine',
 'learning',
 'field',
 'computer',
 'program',
 'said',
 'learn',
 'experience',
 'respect',
 'class',
 'tasks',
 'performance',
 'measure',
 'p',
 'performance',
 'tasks',
 'measured',
 'p',
 'improves',
 'experience',
 'definition',
 'tasks',
 'machine',
 'learning',
 'concerned',
 'offers',
 'fundamentally',
 'operational',
 'definition',
 'rather',
 'defining',
 'field',
 'cognitive',
 'terms',
 'follows',
 'alan',
 'turing',
 'proposal',
 'paper',
 'computing',
 'machinery',
 'intelligence',
 'question',
 'machines',
 'think',
 'replaced',
 'question',
 'machines',
 'thinking',
 'entities',
 'modern',
 'day',
 'machine',
 'learning',
 'two',
 'objectives',
 'classify',
 'data',
 'based',
 'models',
 'developed',
 'purpose',
 'make',
 'predictions',
 'future',
 'outcomes',
 'based',
 'models',
 'hypothetical',
 'algorithm',
 'specific',
 'classifying',
 'data',
 'may',
 'use',
 'computer',
 'vision',
 'moles',
 'coupled',
 'supervised',
 'learning',
 'order',
 'train',
 'classify',
 'cancerous',
 'moles',
 'machine',
 'learning',
 'algorithm',
 'stock',
 'trading',
 'may',
 'inform',
 'trader',
 'future',
 'potential',
 'predictions',
 'artificial',
 'intelligence',
 'edit',
 'machine',
 'learning',
 'subfield',
 'part',
 'machine',
 'learning',
 'subfield',
 'part',
 'subfield',
 'machine',
 'learning',
 'scientific',
 'endeavor',
 'machine',
 'learning',
 'grew',
 'quest',
 'artificial',
 'intelligence',
 'early',
 'days',
 'academic',
 'discipline',
 'researchers',
 'interested',
 'machines',
 'learn',
 'data',
 'attempted',
 'approach',
 'problem',
 'various',
 'symbolic',
 'methods',
 'well',
 'termed',
 'neural',
 'networks',
 'mostly',
 'perceptrons',
 'models',
 'later',
 'found',
 'reinventions',
 'generalized',
 'linear',
 'models',
 'statistics',
 'probabilistic',
 'reasoning',
 'employed',
 'especially',
 'automated',
 'medical',
 'diagnosis',
 'however',
 'increasing',
 'emphasis',
 'logical',
 'knowledge',
 'based',
 'approach',
 'caused',
 'rift',
 'machine',
 'learning',
 'probabilistic',
 'systems',
 'plagued',
 'theoretical',
 'practical',
 'problems',
 'data',
 'acquisition',
 'representation',
 'expert',
 'systems',
 'dominate',
 'statistics',
 'favor',
 'work',
 'symbolic',
 'knowledge',
 'based',
 'learning',
 'continue',
 'within',
 'leading',
 'inductive',
 'logic',
 'programming',
 'statistical',
 'line',
 'research',
 'outside',
 'field',
 'proper',
 'pattern',
 'recognition',
 'information',
 'retrieval',
 'neural',
 'networks',
 'research',
 'abandoned',
 'computer',
 'science',
 'around',
 'time',
 'line',
 'continued',
 'outside',
 'cs',
 'field',
 'connectionism',
 'researchers',
 'disciplines',
 'including',
 'hopfield',
 'rumelhart',
 'hinton',
 'main',
 'success',
 'came',
 'mid',
 'reinvention',
 'backpropagation',
 'machine',
 'learning',
 'ml',
 'reorganized',
 'separate',
 'field',
 'started',
 'flourish',
 'field',
 'changed',
 'goal',
 'achieving',
 'artificial',
 'intelligence',
 'tackling',
 'solvable',
 'problems',
 'practical',
 'nature',
 'shifted',
 'focus',
 'away',
 'symbolic',
 'approaches',
 'inherited',
 'toward',
 'methods',
 'models',
 'borrowed',
 'statistics',
 'probability',
 'theory',
 'difference',
 'ml',
 'frequently',
 'misunderstood',
 'ml',
 'learns',
 'predicts',
 'based',
 'passive',
 'observations',
 'whereas',
 'implies',
 'agent',
 'interacting',
 'environment',
 'learn',
 'actions',
 'maximize',
 'chance',
 'successfully',
 'achieving',
 'goals',
 'many',
 'sources',
 'continue',
 'assert',
 'ml',
 'remains',
 'subfield',
 ...]

Count frequency of words

Class collections.Counter([iterable-or-mapping]) - A Counter is a collection where elements are stored as dictionary keys and their counts are stored as dictionary values.

word_frequencies = collections.Counter(tokens)
print(word_frequencies)

Counter({'learning': 353, 'machine': 204, 'data': 119, 'artificial': 59, 'algorithms': 55, 'training': 52, 'edit': 52, 'retrieved': 52, 'neural': 46, 'model': 43, 'systems': 40, 'intelligence': 37, 'isbn': 37, 'networks': 35, 'computer': 34, 'used': 34, 'algorithm': 32, 'theory': 31, 'software': 31, 'network': 30, 'set': 30, 'supervised': 29, 'analysis': 27, 'models': 27, 'methods': 27, 'classification': 25, 'system': 25, 'mining': 24, 'research': 24, 'based': 24, 'information': 24, 'detection': 22, 'unsupervised': 22, 'computing': 22, 'regression': 21, 'computational': 21, 'example': 21, 'science': 21, 'statistical': 20, 'reinforcement': 20, 'field': 20, 'programming': 20, 'main': 20, 'new': 20, 'knowledge': 19, 'statistics': 19, 'using': 19, 'article': 19, 'journal': 18, 'language': 18, 'feature': 18, 'deep': 18, 'recognition': 18, 'b': 18, 'often': 17, 'may': 17, 'many': 17, 'decision': 16, 'p': 16, 'examples': 16, 'cid': 16, 'tasks': 15, 'pattern': 15, 'learn': 15, 'input': 15, 'conference': 15, 'pp': 15, 'rules': 14, 'linear': 14, 'machines': 14, 'bias': 14, 'related': 14, 'processing': 14, 'pdf': 14, 'press': 14, 'problems': 13, 'dictionary': 13, 'embedded': 13, 'human': 13, 'approach': 13, 'time': 13, 'rule': 13, 'part': 12, 'vector': 12, 'use': 12, 'predictions': 12, 'without': 12, 'perform': 12, 'optimization': 12, 'self': 12, 'genetic': 12, 'performance': 12, 'inputs': 12, 'features': 12, 'techniques': 12, 'process': 12, 'original': 12, 'international': 12, 'anomaly': 11, 'association': 11, 'cs': 11, 'approaches': 11, 'test': 11, 'applications': 11, 'known': 11, 'make': 11, 'sparse': 11, 'representation': 11, 'inductive': 11, 'environment': 11, 'method': 11, 'function': 11, 'trained': 11, 'signal': 11, 'called': 11, 'biases': 11, 'vision': 10, 'ml': 10, 'bayesian': 10, 'problem': 10, 'technology': 10, 'overfitting': 10, 'hardware': 10, 'program': 10, 'two': 10, 'logic': 10, 'learned': 10, 'modeling': 10, 'represented': 10, 'image': 10, 'neurons': 10, 'google': 10, 'introduction': 10, 'issn': 10, 'archived': 10, 'study': 9, 'support': 9, 'arxiv': 9, 'control': 9, 'speech': 9, 'mathematical': 9, 'well': 9, 'given': 9, 'found': 9, 'goal': 9, 'discovery': 9, 'r': 9, 'review': 9, 'racist': 9, 'design': 9, 'pmid': 9, 'order': 8, 'application': 8, 'brain': 8, 'generalization': 8, 'robot': 8, 'probabilistic': 8, 'different': 8, 'accuracy': 8, 'algorithmic': 8, 'output': 8, 'typically': 8, 'variables': 8, 'dimensional': 8, 'g': 8, 'applied': 8, 'large': 8, 'first': 8, 'inference': 8, 'tree': 8, 'springer': 8, 'people': 8, 'university': 8, 'search': 7, 'see': 7, 'semi': 7, 'dimensionality': 7, 'reduction': 7, 'outlier': 7, 'game': 7, 'computers': 7, 'physical': 7, 'open': 7, 'source': 7, 'uses': 7, 'task': 7, 'class': 7, 'specific': 7, 'medical': 7, 'nature': 7, 'predict': 7, 'labels': 7, 'like': 7, 'include': 7, 'interaction': 7, 'real': 7, 'represent': 7, 'fail': 7, 'shapiro': 7, 'health': 7, 'ibm': 7, 'david': 7, 'pmc': 7, 'maint': 7, 'march': 7, 'wikipedia': 6, 'improve': 6, 'clustering': 6, 'prediction': 6, 'engineering': 6, 'trees': 6, 'random': 6, 'general': 6, 'natural': 6, 'ethics': 6, 'history': 6, 'decisions': 6, 'business': 6, 'federated': 6, 'solve': 6, 'various': 6, 'term': 6, 'report': 6, 'terms': 6, 'including': 6, 'observations': 6, 'databases': 6, 'tools': 6, 'space': 6, 'sets': 6, 'complexity': 6, 'negative': 6, 'outputs': 6, 'would': 6, 'cluster': 6, 'labeled': 6, 'w': 6, 'compute': 6, 'behavior': 6, 'representations': 6, 'world': 6, 'classifier': 6, 'web': 6, 'fei': 6, 'york': 6, 'ieee': 6, 'robert': 6, 'eds': 6, 'link': 6, 'cambridge': 6, 'security': 6, 'free': 5, 'experience': 5, 'difference': 5, 'list': 5, 'turing': 5, 'explicitly': 5, 'programmed': 5, 'variety': 5, 'way': 5, 'relationships': 5, 'fields': 5, 'types': 5, 'proprietary': 5, 'work': 5, 'future': 5, 'separate': 5, 'black': 5, 'exist': 5, 'programs': 5, 'certain': 5, 'arthur': 5, 'book': 5, 'learns': 5, 'definition': 5, 'modern': 5, 'researchers': 5, 'automated': 5, 'however': 5, 'logical': 5, 'theoretical': 5, 'within': 5, 'previously': 5, 'unknown': 5, 'loss': 5, 'instances': 5, 'patterns': 5, 'addition': 5, 'computation': 5, 'polynomial': 5, 'results': 5, 'positive': 5, 'categories': 5, 'matrix': 5, 'similarity': 5, 'either': 5, 'changing': 5, 'emotion': 5, 'situation': 5, 'performing': 5, 'neuron': 5, 'connection': 5, 'layers': 5, 'layer': 5, 'microsoft': 5, 'result': 5, 'privacy': 5, 'netflix': 5, 'art': 5, 'watson': 5, 'ethical': 5, 'wikimedia': 5, 'automation': 5, 'november': 5, 'august': 5, 'proceedings': 5, 'survey': 5, 'series': 4, 'svm': 4, 'net': 4, 'hidden': 4, 'kernel': 4, 'glossary': 4, 'datasets': 4, 'outline': 4, 'playing': 4, 'reasoning': 4, 'symbolic': 4, 'languages': 4, 'needed': 4, 'biological': 4, 'predictive': 4, 'limitations': 4, 'neuromorphic': 4, 'external': 4, 'links': 4, 'likely': 4, 'continue': 4, 'involves': 4, 'provided': 4, 'effective': 4, 'help': 4, 'step': 4, 'potential': 4, 'digital': 4, 'samuel': 4, 'nilsson': 4, 'subfield': 4, 'quest': 4, 'leading': 4, 'line': 4, 'probability': 4, 'intelligent': 4, 'considered': 4, 'evaluated': 4, 'ability': 4, 'functions': 4, 'principal': 4, 'michael': 4, 'context': 4, 'instead': 4, 'common': 4, 'best': 4, 'hypothesis': 4, 'complex': 4, 'discovering': 4, 'driving': 4, 'contains': 4, 'associated': 4, 'value': 4, 'area': 4, 'objects': 4, 'recommendation': 4, 'identify': 4, 'clusters': 4, 'reducing': 4, 'component': 4, 'higher': 4, 'manifold': 4, 'high': 4, 'fit': 4, 'paradigm': 4, 'crossbar': 4, 'caa': 4, 'consequence': 4, 'attempt': 4, 'multiple': 4, 'images': 4, 'items': 4, 'particular': 4, 'intrusion': 4, 'normal': 4, 'characteristic': 4, 'scale': 4, 'bioinformatics': 4, 'transactions': 4, 'database': 4, 'ehud': 4, 'additional': 4, 'social': 4, 'target': 4, 'category': 4, 'single': 4, 'mathematics': 4, 'could': 4, 'devices': 4, 'user': 4, 'contain': 4, 'gorillas': 4, 'thus': 4, 'challenges': 4, 'cross': 4, 'validation': 4, 'rate': 4, 'operating': 4, 'openai': 4, 'tensorflow': 4, '_': 4, 'domingos': 4, 'ethem': 4, 'alpaydin': 4, 'development': 4, 'citeseerx': 4, 'september': 4, 'october': 4, 'bozinovski': 4, 'russell': 4, 'stuart': 4, 'norvig': 4, 'hall': 4, 'library': 4, 'james': 4, 'bengio': 4, 'andrew': 4, 'christopher': 4, 'bibcode': 4, 'inc': 4, 'management': 4, 'th': 4, 'july': 4, 'june': 4, 'date': 4, 'news': 4, 'url': 4, 'navigation': 3, 'automatically': 3, 'linguistics': 3, 'online': 3, 'induction': 3, 'means': 3, 'pca': 3, 'graphical': 3, 'markov': 3, 'cognitive': 3, 'q': 3, 'risk': 3, 'goals': 3, 'evolutionary': 3, 'projects': 3, 'build': 3, 'sample': 3, 'medicine': 3, 'email': 3, 'subset': 3, 'closely': 3, 'focuses': 3, 'making': 3, 'domains': 3, 'analytics': 3, 'journals': 3, 'conferences': 3, 'sources': 3, 'basis': 3, 'strategies': 3, 'inferences': 3, 'since': 3, 'last': 3, 'x': 3, 'rather': 3, 'available': 3, 'answers': 3, 'determine': 3, 'train': 3, 'teaching': 3, 'representative': 3, 'duda': 3, 'hart': 3, 'tom': 3, 'mitchell': 3, 'formal': 3, 'studied': 3, 'said': 3, 'respect': 3, 'measure': 3, 'concerned': 3, 'question': 3, 'developed': 3, 'especially': 3, 'diagnosis': 3, 'practical': 3, 'retrieval': 3, 'actions': 3, 'maximize': 3, 'properties': 3, 'learner': 3, 'basic': 3, 'kdd': 3, 'key': 3, 'cannot': 3, 'due': 3, 'pre': 3, 'unseen': 3, 'current': 3, 'according': 3, 'jordan': 3, 'generally': 3, 'distribution': 3, 'three': 3, 'feedback': 3, 'desired': 3, 'find': 3, 'structure': 3, 'dynamic': 3, 'white': 3, 'array': 3, 'sometimes': 3, 'limited': 3, 'values': 3, 'similar': 3, 'visual': 3, 'density': 3, 'estimation': 3, 'subsets': 3, 'members': 3, 'graph': 3, 'unlabeled': 3, 'exact': 3, 'number': 3, 'low': 3, 'regularization': 3, 'fold': 3, 'adaptive': 3, 'situations': 3, 'action': 3, 'update': 3, 'several': 3, 'better': 3, 'components': 3, 'generating': 3, 'necessarily': 3, 'tensor': 3, 'require': 3, 'video': 3, 'built': 3, 'noise': 3, 'rare': 3, 'fraud': 3, 'text': 3, 'able': 3, 'detect': 3, 'least': 3, 'inspired': 3, 'strong': 3, 'apply': 3, 'marketing': 3, 'market': 3, 'sequence': 3, 'facts': 3, 'plotkin': 3, 'proving': 3, 'represents': 3, 'ann': 3, 'collection': 3, 'units': 3, 'times': 3, 'attention': 3, 'translation': 3, 'item': 3, 'variable': 3, 'whether': 3, 'efficient': 3, 'uncertainty': 3, 'selection': 3, 'good': 3, 'need': 3, 'users': 3, 'service': 3, 'society': 3, 'integrated': 3, 'distributed': 3, 'send': 3, 'efficiency': 3, 'mobile': 3, 'internet': 3, 'media': 3, 'prize': 3, 'joint': 3, 'wall': 3, 'street': 3, 'diagnostic': 3, 'green': 3, 'lack': 3, 'evaluation': 3, 'car': 3, 'evidence': 3, 'systematic': 3, 'still': 3, 'chatbot': 3, 'adversarial': 3, 'true': 3, 'toc': 3, 'european': 3, 'corpora': 3, 'personal': 3, 'improving': 3, 'cloud': 3, 'emulate': 3, 'approximate': 3, 'platform': 3, 'enterprise': 3, 'differentiable': 3, 'mcgraw': 3, 'hill': 3, 'martin': 3, 'f': 3, 'bishop': 3, 'vii': 3, 'www': 3, 'vol': 3, 'stat': 3, 'nd': 3, 'prentice': 3, 'yoshua': 3, 'second': 3, 'org': 3, 'authors': 3, 'cybernetics': 3, 'lee': 3, 'ng': 3, 'kumar': 3, 'acm': 3, 'fleer': 3, 'moringen': 3, 'ritter': 3, 'morgan': 3, 'kaufmann': 3, 'hui': 3, 'blog': 3, 'tech': 3, 'things': 3, 'reviews': 3, 'england': 3, 'status': 3, 'harvard': 3, 'policy': 3, 'bostrom': 3, 'nick': 3, 'implementing': 3, 'ray': 3, 'synced': 3, 'ian': 3, 'electronics': 3, 'synthesis': 3, 'commons': 3, 'electronic': 3, 'organization': 3, 'identifiersarticles': 3, 'encyclopedia': 2, 'jump': 2, 'acquisition': 2, 'structured': 2, 'forest': 2, 'nn': 2, 'bayes': 2, 'logistic': 2, 'perceptron': 2, 'cure': 2, 'hierarchical': 2, 'factor': 2, 'conditional': 2, 'multilayer': 2, 'restricted': 2, 'convolutional': 2, 'transformer': 2, 'sarsa': 2, 'variance': 2, 'empirical': 2, 'minimization': 2, 'neurips': 2, 'icml': 2, 'articles': 2, 'vte': 2, 'major': 2, 'planning': 2, 'philosophy': 2, 'timeline': 2, 'seen': 2, 'wide': 2, 'filtering': 2, 'difficult': 2, 'develop': 2, 'implementations': 2, 'mimics': 2, 'working': 2, 'across': 2, 'referred': 2, 'overview': 2, 'assessments': 2, 'editions': 2, 'references': 2, 'reading': 2, 'past': 2, 'sun': 2, 'every': 2, 'morning': 2, 'days': 2, 'species': 2, 'chance': 2, 'simple': 2, 'assigned': 2, 'required': 2, 'hand': 2, 'practice': 2, 'discipline': 2, 'employs': 2, 'fully': 2, 'cases': 2, 'vast': 2, 'numbers': 2, 'label': 2, 'correct': 2, 'dataset': 2, 'digits': 2, 'coined': 2, 'dealing': 2, 'mostly': 2, 'interest': 2, 'continued': 2, 'recognize': 2, 'letters': 2, 'improves': 2, 'defining': 2, 'follows': 2, 'alan': 2, 'machinery': 2, 'thinking': 2, 'objectives': 2, 'classify': 2, 'outcomes': 2, 'moles': 2, 'scientific': 2, 'academic': 2, 'perceptrons': 2, 'later': 2, 'employed': 2, 'increasing': 2, 'dominate': 2, 'outside': 2, 'disciplines': 2, 'hinton': 2, 'backpropagation': 2, 'changed': 2, 'achieving': 2, 'toward': 2, 'frequently': 2, 'predicts': 2, 'agent': 2, 'view': 2, 'significantly': 2, 'much': 2, 'ecml': 2, 'pkdd': 2, 'assumptions': 2, 'usually': 2, 'express': 2, 'correctly': 2, 'samples': 2, 'active': 2, 'topic': 2, 'methodological': 2, 'principles': 2, 'long': 2, 'call': 2, 'overall': 2, 'breiman': 2, 'less': 2, 'adopted': 2, 'objective': 2, 'enables': 2, 'produce': 2, 'sufficiently': 2, 'accurate': 2, 'bounds': 2, 'error': 2, 'underlying': 2, 'increased': 2, 'decreases': 2, 'kinds': 2, 'show': 2, 'broad': 2, 'teacher': 2, 'towards': 2, 'opponent': 2, 'rewards': 2, 'tries': 2, 'divides': 2, 'boundary': 2, 'consists': 2, 'numerical': 2, 'file': 2, 'verification': 2, 'learningsee': 2, 'commonalities': 2, 'presence': 2, 'finding': 2, 'encompasses': 2, 'drawn': 2, 'defined': 2, 'estimated': 2, 'falls': 2, 'missing': 2, 'yet': 2, 'amount': 2, 'resulting': 2, 'agents': 2, 'operations': 2, 'mdp': 2, 'autonomous': 2, 'dimension': 2, 'popular': 2, 'smaller': 2, 'along': 2, 'assumption': 2, 'meta': 2, 'dominant': 2, 'introduced': 2, 'advice': 2, 'emotions': 2, 'cognition': 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Create Word Cloud

#!sudo -E pip install wordcloud

from wordcloud import WordCloud
import matplotlib.pyplot as plt

word_cloud = WordCloud(
    width = 800, 
    height = 500, 
    background_color ='white').generate_from_frequencies(word_frequencies)

plt.imshow(word_cloud, interpolation='bilinear')
plt.axis("off")
plt.show()

Show wordcloud

word_cloud = WordCloud(
    width = 800, 
    height = 500).generate_from_frequencies(word_frequencies)

plt.imshow(word_cloud, interpolation='bilinear')
plt.axis("off")
plt.show()

Save in png

word_cloud.to_file("machine_learning.png")

<wordcloud.wordcloud.WordCloud at 0x7f2d8c08e910>

Display the saved image

import matplotlib.pyplot as plt
import matplotlib.image as img

plt.figure(figsize=[20,10])

# read png image file
im = img.imread('machine_learning.png')
  
#show image
plt.imshow(im)
plt.axis("off")
plt.show()