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
- Web scraping
- Use of regular expression to clean text
- NLTK Tokenization
- Use of STOP WORDS
- Get the word frequencies
- Create WordCloud
- Plot WordCloud with the help of Matplotlib
In [1]:
from bs4 import BeautifulSoup
from urllib.request import Request, urlopen
import re
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.
In [2]:
url = "https://en.wikipedia.org/wiki/Machine_learning"
headers = {'User-Agent': 'Mozilla/5.0'}
In [3]:
req = Request(url, headers = headers )
page = urlopen(req)
page
Out[3]:
<http.client.HTTPResponse at 0x7f2dc0238310>
In [4]:
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'
In [5]:
# kill all script and style elements
for script in soup(["script", "style"]):
script.extract()
In [6]:
text = soup.get_text().strip()
print(text[:100])
Machine learning - Wikipedia Machine learning From Wikiped
In [7]:
#remove extra space from left and right and make it lower
text = text.strip()
text = text.lower()
text[:100]
Out[7]:
'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'
In [8]:
#\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
In [9]:
import collections
from nltk.tokenize.toktok import ToktokTokenizer
from nltk.corpus import stopwords
In [10]:
#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', 'learning', 'bayesian', 'networks', 'evolutionary', 'algorithms', 'philosophy', 'chinese', 'room', 'friendly', 'ai', 'control', 'problem', 'takeover', 'ethics', 'existential', 'risk', 'turing', 'test', 'history', 'timeline', 'progress', 'ai', 'winter', 'technology', 'applications', 'projects', 'programming', 'languages', 'glossary', 'glossary', 'vte', 'machine', 'learning', 'ml', 'is', 'the', 'study', 'of', 'computer', 'algorithms', 'that', 'can', 'improve', 'automatically', 'through', 'experience', 'and', 'by', 'the', 'use', 'of', 'data', 'it', 'is', 'seen', 'as', 'a', 'part', 'of', 'artificial', 'intelligence', 'machine', 'learning', 'algorithms', 'build', 'a', 'model', 'based', 'on', 'sample', 'data', 'known', 'as', 'training', 'data', 'in', 'order', 'to', 'make', 'predictions', 'or', 'decisions', 'without', 'being', 'explicitly', 'programmed', 'to', 'do', 'so', 'machine', 'learning', 'algorithms', 'are', 'used', 'in', 'a', 'wide', 'variety', 'of', 'applications', 'such', 'as', 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In [ ]:
In [11]:
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'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', ...]
In [ ]:
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.
In [12]:
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, 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2, 'memory': 2, 'following': 2, 'secondary': 2, 'environments': 2, 'behavioral': 2, 'receives': 2, 'initial': 2, 'aim': 2, 'useful': 2, 'technique': 2, 'allows': 2, 'factorization': 2, 'forms': 2, 'constraint': 2, 'meaning': 2, 'multilinear': 2, 'subspace': 2, 'discover': 2, 'level': 2, 'explain': 2, 'observed': 2, 'attempts': 2, 'alternative': 2, 'heuristic': 2, 'svd': 2, 'already': 2, 'sparsely': 2, 'identification': 2, 'majority': 2, 'structural': 2, 'errors': 2, 'anomalies': 2, 'deviations': 2, 'aggregated': 2, 'representing': 2, 'instance': 2, 'generated': 2, 'store': 2, 'collectively': 2, 'contrast': 2, 'commonly': 2, 'concept': 2, 'agrawal': 2, 'imieliński': 2, 'swami': 2, 'transaction': 2, 'mathrm': 2, 'onions': 2, 'potatoes': 2, 'customer': 2, 'areas': 2, 'continuous': 2, 'ilp': 2, 'background': 2, 'hypotheses': 2, 'functional': 2, 'foundation': 2, 'setting': 2, 'philosophical': 2, 'nodes': 2, 'another': 2, 'connectionist': 2, 'considering': 2, 'connected': 2, 'edges': 2, 'weight': 2, 'increases': 2, 'threshold': 2, 'signals': 2, 'biology': 2, 'board': 2, 'games': 2, 'processes': 2, 'successful': 2, 'go': 2, 'branches': 2, 'leaves': 2, 'discrete': 2, 'lead': 2, 'svms': 2, 'although': 2, 'trick': 2, 'implicitly': 2, 'relationship': 2, 'form': 2, 'trendline': 2, 'excel': 2, 'even': 2, 'rain': 2, 'influences': 2, 'sprinkler': 2, 'influence': 2, 'belief': 2, 'directed': 2, 'acyclic': 2, 'diseases': 2, 'symptoms': 2, 'sequences': 2, 'engineers': 2, 'sensor': 2, 'collected': 2, 'individual': 2, 'derived': 2, 'biased': 2, 'impacts': 2, 'teams': 2, 'centralized': 2, 'quality': 2, 'financial': 2, 'embedding': 2, 'monitoring': 2, 'services': 2, 'team': 2, 'made': 2, 'big': 2, 'firm': 2, 'co': 2, 'vinod': 2, 'khosla': 2, 'doctors': 2, 'next': 2, 'fine': 2, 'paintings': 2, 'among': 2, 'created': 2, 'covid': 2, 'recently': 2, 'thermal': 2, 'behaviour': 2, 'deliver': 2, 'reasons': 2, 'wrong': 2, 'resources': 2, 'uber': 2, 'failed': 2, 'pedestrian': 2, 'killed': 2, 'healthcare': 2, 'burden': 2, 'reduce': 2, 'sensitivity': 2, 'issues': 2, 'tool': 2, 'accordance': 2, 'brown': 2, 'horses': 2, 'might': 2, 'humans': 2, 'pixel': 2, 'holdout': 2, 'comparison': 2, 'respectively': 2, 'bootstrap': 2, 'investigators': 2, 'false': 2, 'rates': 2, 'numerators': 2, 'denominators': 2, 'total': 2, 'shows': 2, 'roc': 2, 'uk': 2, 'george': 2, 'previous': 2, 'job': 2, 'hiring': 2, 'applicants': 2, 'technical': 2, 'rely': 2, 'professionals': 2, 'designed': 2, 'public': 2, 'united': 2, 'states': 2, 'dilemma': 2, 'provide': 2, 'patients': 2, 'owners': 2, 'requires': 2, 'advances': 2, 'gpus': 2, 'alexnet': 2, 'alphazero': 2, 'electrically': 2, 'adjustable': 2, 'material': 2, 'synapse': 2, 'memristor': 2, 'wearable': 2, 'transferring': 2, 'acceleration': 2, 'keras': 2, 'root': 2, 'pytorch': 2, 'modeler': 2, 'oracle': 2, 'miner': 2, 'aaai': 2, 'volume': 2, 'publication': 2, 'john': 2, 'circuits': 2, 'exploration': 2, 'via': 2, 'friedman': 2, 'jerome': 2, 'chapter': 2, 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In [ ]:
In [13]:
#!sudo -E pip install wordcloud
In [14]:
from wordcloud import WordCloud
import matplotlib.pyplot as plt
In [15]:
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()
In [16]:
word_cloud = WordCloud(
width = 800,
height = 500).generate_from_frequencies(word_frequencies)
plt.imshow(word_cloud, interpolation='bilinear')
plt.axis("off")
plt.show()
In [24]:
word_cloud.to_file("machine_learning.png")
Out[24]:
<wordcloud.wordcloud.WordCloud at 0x7f2d8c08e910>
In [22]:
import matplotlib.pyplot as plt
import matplotlib.image as img
In [25]:
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()