Data Science, Deep Learning, Machine Learning, Recuperação da Informação

Materiais sobre Inteligência Artificial, Machine Learning, Statistics e etc…

Abaixo uma coleção de links de materiais de diversos assuntos relacionados a Inteligência Artificial, Machine Learning, Statistics, Processamento de Linguagem Natural e etc…

Dicas diversas

Manipulando Strings com Python
https://www.linkedin.com/pulse/manipulando-strings-com-python-fernanda-santos

140 Machine Learning Formulas
https://www.datasciencecentral.com/profiles/blogs/140-machine-learning-formulas

40 Techniques Used by Data Scientists
https://www.datasciencecentral.com/profiles/blogs/40-techniques-used-by-data-scientists

24 Uses of Statistical Modeling (Part I | Part II)
https://www.datasciencecentral.com/profiles/blogs/top-20-uses-of-statistical-modeling
https://www.datasciencecentral.com/profiles/blogs/24-uses-of-statistical-modeling-part-ii

29 Statistical Concepts Explained in Simple English
https://www.datasciencecentral.com/profiles/blogs/32-statistical-concepts-explained-in-simple-english-part-12

Machine Learning with SQL
https://towardsdatascience.com/machine-learning-with-sql-ae46b1fe78a9

How Statistical Norms Improve Modeling
https://towardsdatascience.com/norms-penalties-and-multitask-learning-2f1db5f97c1f

12 Algorithms Every Data Scientist Should Know
https://www.datasciencecentral.com/profiles/blogs/12-algorithms-every-data-scientist-should-know

Boost your data science skills. Learn linear algebra.
https://www.datasciencecentral.com/profiles/blogs/boost-your-data-science-skills-learn-linear-algebra

The History and Future of Machine Learning at Reddit
https://medium.com/@ODSC/the-history-and-future-of-machine-learning-at-reddit-d1cb17e06591

Fundamental Techniques of Feature Engineering for Machine Learning
https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114

Data science concepts you need to know!
https://towardsdatascience.com/introduction-to-statistics-e9d72d818745

Interpreting machine learning models
https://towardsdatascience.com/interpretability-in-machine-learning-70c30694a05f

Interpretando Modelos de Machine Learning
https://medium.com/beacon-insight/interpretando-modelos-de-machine-learning-96db60781354

Building a Machine Learning Model When Data Isn’t Available
https://medium.com/towards-artificial-intelligence/building-a-machine-learning-model-when-data-isnt-available-fce8d20f0fd0

Productionizing your Machine Learning model
https://towardsdatascience.com/productionizing-your-machine-learning-model-221468b0726d

Feature Transformation for Machine Learning, a Beginners Guide (*)
https://medium.com/vickdata/four-feature-types-and-how-to-transform-them-for-machine-learning-8693e1c24e80

Understanding Value Of Correlations In Data Science Projects
https://medium.com/fintechexplained/did-you-know-the-importance-of-finding-correlations-in-data-science-1fa3943debc2

Programming Skills, A Complete Roadmap for Learning Data Science
https://medium.com/vickdata/programming-skills-a-complete-roadmap-for-learning-data-science-part-1-7913b289751b

Version Control for Data Scientists: A Hands-on Introduction
https://towardsdatascience.com/version-control-for-data-scientists-a-hands-on-introduction-4a9f3d33edfd

Choosing a Machine Learning Model
https://medium.com/towards-artificial-intelligence/choosing-a-machine-learning-model-daebb39145a

How to find Feature importances for BlackBox Models?
https://towardsdatascience.com/how-to-find-feature-importances-for-blackbox-models-c418b694659d

Matérias e Artigos

O que as áreas de BI e Data Science têm a ver com a área de Negócios?
https://www.linkedin.com/pulse/o-que-%C3%A1rea-de-neg%C3%B3cios-tem-ver-com-bi-e-data-science-andr%C3%A9a-longarini/

10 CENÁRIOS DE COMO ENTREGAR UM PROJETO DE MACHINE LEARNING
http://datascienceacademy.com.br/blog/10-cenarios-de-como-entregar-um-projeto-de-machine-learning/

COMO PRECIFICAR UM PROJETO DE DATA SCIENCE, MACHINE LEARNING OU IA
http://datascienceacademy.com.br/blog/como-precificar-um-projeto-de-data-science-machine-learning-ou-ia/

5 APLICAÇÕES DE INTELIGÊNCIA ARTIFICIAL EM MEDICINA
http://datascienceacademy.com.br/blog/5-aplicacoes-de-inteligencia-artificial-em-medicina/

INTELIGÊNCIA ARTIFICIAL EM MEDICINA: APLICAÇÕES, IMPLICAÇÕES E LIMITAÇÕES
http://datascienceacademy.com.br/blog/inteligencia-artificial-em-medicina-aplicacoes-implicacoes-e-limitacoes/

A IA PODE LER UMA RESSONÂNCIA MAGNÉTICA CARDIOVASCULAR EM 4 SEGUNDOS. AINDA PRECISAMOS DE AVALIAÇÃO HUMANA?
http://datascienceacademy.com.br/blog/a-ia-pode-ler-uma-ressonancia-magnetica-cardiovascular-em-4-segundos-ainda-precisamos-de-avaliacao-humana/

SEGMENTAÇÃO DE IMAGENS MÉDICAS COM DEEP LEARNING
http://datascienceacademy.com.br/blog/segmentacao-de-imagens-medicas-com-deep-learning/

INTELIGÊNCIA ARTIFICIAL APRENDE QUÍMICA PARA PREVER COMO FAZER MEDICAMENTOS
http://datascienceacademy.com.br/blog/inteligencia-artificial-aprende-quimica-para-prever-como-fazer-medicamentos/

12 LIÇÕES APRENDIDAS EM ENTREVISTAS PARA CIENTISTA DE DADOS
http://datascienceacademy.com.br/blog/12-licoes-aprendidas-em-entrevistas-para-cientista-de-dados/

Ciência de Dados e o Cientista de Dados
https://medium.com/beacon-insight/ci%C3%AAncia-de-dados-e-o-cientista-de-dados-72634fcc1a4c

DIFERENÇAS ENTRE RPA, IA E MACHINE LEARNING
http://datascienceacademy.com.br/blog/diferencas-entre-rpa-ia-e-machine-learning/

Por Que Você Deve Aprender Álgebra Linear Para Trabalhar com Machine Learning?
http://www.cienciaedados.com/por-que-voce-deve-aprender-algebra-linear-para-trabalhar-com-machine-learning/?utm_campaign=shareaholic&utm_medium=twitter&utm_source=socialnetwork

Matemática, Probabilidade e Estatística 

Probability Theory
https://towardsdatascience.com/probability-fundamentals-of-machine-learning-part-1-a156b4703e69

Probability Learning I : Bayes’ Theorem
https://towardsdatascience.com/probability-learning-i-bayes-theorem-708a4c02909a

Common Probability Distributions: The Data Scientist’s Crib Sheet
https://medium.com/@srowen/common-probability-distributions-347e6b945ce4

Probabilistic Model Selection with AIC, BIC, and MDL
https://machinelearningmastery.com/probabilistic-model-selection-measures/

Descriptive Statistics with Python
https://medium.com/dataseries/descriptive-statistics-with-python-75e2b1249e8d

9 Off-the-beaten-path Statistical Science Topics with Interesting Applications
https://www.datasciencecentral.com/profiles/blogs/9-off-th-beaten-path-statistical-science-topics

15 Statistical Hypothesis Tests in Python (Cheat Sheet)
https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/

Machine Learning Vs. Statistics
https://www.datasciencecentral.com/profiles/blogs/machine-learning-vs-statistics

Book: Statistics for Non-Statisticians
https://www.datasciencecentral.com/profiles/blogs/book-statistics-for-non-statisticians

The Death of the Statistical Tests of Hypotheses
https://www.datasciencecentral.com/profiles/blogs/the-death-of-the-statistical-test-of-hypothesis

A Beautiful Probability Theorem
https://www.datasciencecentral.com/profiles/blogs/a-beautiful-probability-theorem

High-performance mathematical paradigms in Python
https://medium.com/@harshit_tyagi/understanding-high-performance-paradigms-in-python-738049946e8b

A Simple Introduction to Complex Stochastic Processes
https://www.datasciencecentral.com/profiles/blogs/a-simple-introduction-to-complex-stochastic-processes

Probability Scoring Methods in Python
https://machinelearningmastery.com/how-to-score-probability-predictions-in-python/

 

Ferramentas (Tools)

Python Tools for a Beginner Data Scientist
https://towardsdatascience.com/python-tools-for-a-beginner-data-scientist-39b3b9a4303a

 

Algoritmos

Top 10 Machine Learning Algorithms for Beginners
https://www.kdnuggets.com/2017/10/top-10-machine-learning-algorithms-beginners.html

Extracting Data from PDF File Using Python and R
https://medium.com/towards-artificial-intelligence/extracting-data-from-pdf-file-using-python-and-r-4ed8826bc5a1

A Simple Example of Pipeline in Machine Learning with Scikit-learn
https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-with-scikit-learn-e726ffbb6976

Introduction to Information Extraction using Python and spaCy
https://medium.com/analytics-vidhya/introduction-to-information-extraction-using-python-and-spacy-858f5d6416ca

Machine Learning Explained: Understanding Supervised, Unsupervised, and Reinforcement Learning
https://www.datasciencecentral.com/profiles/blogs/machine-learning-explained-understanding-supervised-unsupervised

Diving deeper into Reinforcement Learning with Q-Learning
https://medium.com/free-code-camp/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe

Principal Component Analysis: Your Tutorial and Code
https://towardsdatascience.com/principal-component-analysis-your-tutorial-and-code-9719d3d3f376

Using 3D visualizations to tune hyperparameters in ML models
https://towardsdatascience.com/using-3d-visualizations-to-tune-hyperparameters-of-ml-models-with-python-ba2885eab2e9

Where next? After SVMs, CNNs and Word Embeddings
https://medium.com/hackernoon/where-next-after-svms-cnns-and-word-embeddings-2becb8576cba

30 Helpful Python Snippets That You Can Learn in 30 Seconds or Less
https://towardsdatascience.com/30-helpful-python-snippets-that-you-can-learn-in-30-seconds-or-less-69bb49204172s

14 Great Articles About Cross-Validation, Model Fitting and Selection
https://www.datasciencecentral.com/profiles/blogs/14-great-articles-about-cross-validation-model-fitting-and-select

Outlier detection 101: Median and Interquartile range.
https://medium.com/@davidnh8/outlier-detection-101-median-and-interquartile-range-cc9dde94c0ac

How to Reduce Overfitting Using Weight Constraints in Keras
https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-neural-networks-with-weight-constraints-in-keras/

Activation Regularization in Deep Learning
https://machinelearningmastery.com/activation-regularization-for-reducing-generalization-error-in-deep-learning-neural-networks/

Cocktail Party Source Separation Using Deep Learning Networks
https://www.mathworks.com/help/deeplearning/examples/cocktail-party-source-separation-using-deep-learning-networks.html

Building and deploying a machine learning model with automated ML on Azure.
https://towardsdatascience.com/building-and-deploying-a-machine-learning-model-with-automated-ml-on-azure-b586a0e7d448

From shallow to deep learning in fraud
https://eng.lyft.com/from-shallow-to-deep-learning-in-fraud-9dafcbcef743

Using Algorithms to Detect Fake News – The State of the Art
https://www.datasciencecentral.com/profiles/blogs/using-algorithms-to-detect-fake-news-the-state-of-the-art

TOP MACHINE LEARNING ALGORITHMS FOR PREDICTIONS. A SHORT OVERVIEW.
https://www.aisoma.de/top-machine-learning-algorithms-for-predictions-a-short-overview/

A Semi-Supervised Classification Algorithm using Markov Chain and Random Walk in R
https://www.datasciencecentral.com/profiles/blogs/a-semi-supervised-classification-algorithm-using-markov-chain-and

Modelagem Preditiva para Problemas de Classificação
http://fbarth.net.br/ml/machine/learning/2015/02/27/modelos-preditivos-classificacao.html

Machine Learning for Diabetes with Python (Previsão)
https://datascienceplus.com/machine-learning-for-diabetes-with-python/

Comparison of Lightweight Document Classification Models
https://medium.com/dataseries/comparison-of-lightweight-document-classification-models-9f509361e52

Using LDA Topic Models as a Classification Model Input
https://towardsdatascience.com/unsupervised-nlp-topic-models-as-a-supervised-learning-input-cf8ee9e5cf28

Latent Dirichlet Allocation (LDA) with Python
https://rstudio-pubs-static.s3.amazonaws.com/79360_850b2a69980c4488b1db95987a24867a.html

Linear Discriminant Analysis In Python
https://towardsdatascience.com/linear-discriminant-analysis-in-python-76b8b17817c2

Stacking models for improved predictions: A case study for housing prices
https://www.datasciencecentral.com/profiles/blogs/stacking-models-for-improved-predictions-a-case-study-for-housing

Difference Between Classification and Regression in Machine Learning
https://machinelearningmastery.com/classification-versus-regression-in-machine-learning/

Ensemble Learning: When everybody takes a guess…I guess!
https://medium.com/diogo-menezes-borges/ensemble-learning-when-everybody-takes-a-guess-i-guess-ec35f6cb4600

Classificação

Using Transfer Learning and Pre-trained Language Models to Classify Spam
https://heartbeat.fritz.ai/using-transfer-learning-and-pre-trained-language-models-to-classify-spam-549fc0f56c20

Implementation of 17 classification algorithms in R
https://www.datasciencecentral.com/profiles/blogs/implemetation-of-17-classification-algorithms-in-r

Understanding Classification Thresholds Using Isocurves
https://towardsdatascience.com/understanding-classification-thresholds-using-isocurves-9e5e7e00e5a2

Warm up in Classification Modeling
https://medium.com/datadriveninvestor/warm-up-in-classification-modeling-dd4a4f100e8b

Decision Trees, Classification & Interpretation Using SciKit-Learn
https://www.datasciencecentral.com/profiles/blogs/decision-trees-classification-interpretation-using-scikit-learn

Decision tree vs. linearly separable or non-separable pattern
http://www.analyticbridge.datasciencecentral.com/profiles/blogs/decision-tree-vs-linearly-separable-or-non-separable-pattern

Decision Tree Classifier from Scratch: Classifying Student’s Knowledge Level
https://towardsdatascience.com/decision-tree-classifier-from-scratch-classifying-students-knowledge-level-c810876d6c8f

How To Implement The Decision Tree Algorithm From Scratch In Python
https://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/

Decision Trees: Which feature to split on?
https://medium.com/analytics-vidhya/decision-trees-which-feature-to-split-on-91083fc32279

How Random Forest Algorithm Works in Machine Learning
https://medium.com/@Synced/how-random-forest-algorithm-works-in-machine-learning-3c0fe15b6674

Bagging and Random Forest Ensemble Algorithms for Machine Learning
https://machinelearningmastery.com/bagging-and-random-forest-ensemble-algorithms-for-machine-learning/

Tuning the parameters of your Random Forest model
https://www.analyticsvidhya.com/blog/2015/06/tuning-random-forest-model/?utm_source=linkedin.com&utm_medium=social

Decision Tree vs Random Forest vs Gradient Boosting Machines: Explained Simply
https://www.datasciencecentral.com/profiles/blogs/decision-tree-vs-random-forest-vs-boosted-trees-explained

An Implementation and Explanation of the Random Forest in Python
https://towardsdatascience.com/an-implementation-and-explanation-of-the-random-forest-in-python-77bf308a9b76

SVM in Practice
https://www.datasciencecentral.com/profiles/blogs/svm-in-practice

Understanding Support Vector Machines: A Primer
https://appliedmachinelearning-blog.cdn.ampproject.org/c/s/appliedmachinelearning.blog/2017/03/09/understanding-support-vector-machines-a-primer/amp/

Principal Component Analysis and SVM in a Pipeline with Python
https://towardsdatascience.com/visualizing-support-vector-machine-decision-boundary-69e7591dacea

A Method for Building a Strong Baseline Text Classifier
https://medium.com/@dhoeschele/a-method-for-building-a-strong-baseline-text-classifier-53451ee6c250

Text Classification — RNN’s or CNN’s?
https://towardsdatascience.com/text-classification-rnns-or-cnn-s-98c86a0dd361

Image Classification With TensorFlow 2.0 ( Without Keras )
https://becominghuman.ai/image-classification-with-tensorflow-2-0-without-keras-e6534adddab2

Naive Bayes

Naive Bayes classification from Scratch in Python
https://medium.com/machine-learning-algorithms-from-scratch/naive-bayes-classification-from-scratch-in-python-e3a48bf5f91a

Loan Prediction – Using PCA and Naive Bayes Classification with R
https://www.datasciencecentral.com/profiles/blogs/loan-prediction-using-pca-and-naive-bayes-classification-with-r

6 Easy Steps to Learn Naive Bayes Algorithm (with code in Python)
https://www.datasciencecentral.com/profiles/blogs/6-easy-steps-to-learn-naive-bayes-algorithm-with-code-in-python

 

Clustering | K-means

K-means – Example
https://people.revoledu.com/kardi/tutorial/kMean/NumericalExample.htm

Get the Optimal K in K-Means Clustering
https://medium.com/towards-artificial-intelligence/get-the-optimal-k-in-k-means-clustering-d45b5b8a4315

An Introduction to Clustering and different methods of clustering
https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-clustering-and-different-methods-of-clusteri-1

Distributed Vector Representation : Simplified
https://towardsdatascience.com/distributed-vector-representation-simplified-55bd2965333e

My Experiments In Replacing Deep Learning Backpropagation (SGD) With A Genetic Algorithm
https://towardsdatascience.com/my-experiments-in-replacing-deep-learning-backpropagation-sgd-with-a-genetic-algorithm-c6e308382926

Deduplication
https://towardsdatascience.com/deduplication-deduplication-1d1414ffb4d2

What is Categorical Data?
https://medium.com/machine-learning-eli5/dealing-with-categorical-data-f4c8556cbda0

 

Neural Networks

Advanced Topics in Neural Networks
https://towardsdatascience.com/advanced-topics-in-neural-networks-f27fbcc638ae

A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)
https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f

Illustrated Guide to LSTM’s and GRU’s: A step by step explanation
https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21

DeepNLP – Recurrent Neural Networks with Math.
https://medium.com/deep-math-machine-learning-ai/chapter-10-deepnlp-recurrent-neural-networks-with-math-c4a6846a50a2

A simple neural network with Python and Keras
https://www.datasciencecentral.com/profiles/blogs/a-simple-neural-network-with-python-and-keras

Recurrent Neural Networks by Example in Python
https://towardsdatascience.com/recurrent-neural-networks-by-example-in-python-ffd204f99470

A Beginner’s Guide to Neural Networks with Python and SciKit Learn 0.18!
http://www.kdnuggets.com/2016/10/beginners-guide-neural-networks-python-scikit-learn.html

The Perceptron Algorithm explained with Python code
https://www.analyticbridge.datasciencecentral.com/profiles/blogs/the-perceptron-algorithm-explained-with-python-code

Análise de sentimento com Multilayer Perceptron Model baseado em Bag-of-Words
https://hackinganalytics.com/2017/10/27/analise-de-sentimento-com-multilayer-perceptron-model-baseado-em-bag-of-words/amp/

Sentiment Classification with Natural Language Processing on LSTM
https://blog.usejournal.com/sentiment-classification-with-natural-language-processing-on-lstm-4dc0497c1f19

Solving classic unsupervised learning problems with deep neural networks
https://towardsdatascience.com/solving-classic-unsupervised-learning-problems-with-deep-neural-networks-768adb892201

 

Linear Regression | Gradiente Descendente

Linear Regression Model
https://towardsdatascience.com/linear-regression-model-899558ba0fc4

PyTorch: Linear and Logistic Regression Models
https://medium.com/biaslyai/pytorch-linear-and-logistic-regression-models-5c5f0da2cb9

Logistic Regression Model Tuning with scikit-learn
https://towardsdatascience.com/logistic-regression-model-tuning-with-scikit-learn-part-1-425142e01af5

Regression, Logistic Regression and Maximum Entropy
http://www.analyticbridge.com/profiles/blogs/regression-logistic-regression-and-maximum-entropy

A Complete Tutorial on Linear Regression with R
http://www.datasciencecentral.com/profiles/blogs/a-complete-tutorial-on-linear-regression-with-r

Getting Started with Regression in R
http://www.datasciencecentral.com/profiles/blogs/getting-started-with-regression-in-r

What is Regression Analysis?
https://www.datasciencecentral.com/profiles/blogs/what-is-regression-analysis

Modelo de Regressão Logística Binária
http://www.estatisticacomr.uff.br/?p=598

Logistic Regression Example in Python (Source Code Included)
https://www.linkedin.com/pulse/logistic-regression-example-python-source-code-lillian-pierson-p-e-

Gradiente Descendente
https://matheusfacure.github.io/2017/02/20/MQO-Gradiente-Descendente/

What in god’s name is Gradient Descent?
https://medium.com/diogo-menezes-borges/what-is-gradient-descent-235a6c8d26b0

 

Processamento de Linguagem Natural (Natural Language Process)

Labeling with Active Learning
https://www.datasciencecentral.com/profiles/blogs/labeling-with-active-learning

[NLP] The Future of Natural Language Processing
https://towardsdatascience.com/the-future-of-natural-language-processing-2fb35d6ed11e

[NLP] for Low-Resource Settings
https://medium.com/sciforce/nlp-for-low-resource-settings-52e199779a79

[NLP] What is GloVe?
https://towardsdatascience.com/emnlp-what-is-glove-part-v-fa888272c290

[NLP] The well-definedness of a least common subsequence (LCS) distance metric.
https://towardsdatascience.com/nlp-the-well-definedness-of-a-least-common-subsequence-lcs-distance-metric-45d743658c87

[NLP] Text Data To Numbers
https://medium.com/fintechexplained/nlp-text-data-to-numbers-d28d32294d2e

[NLP] Stemming? Lemmatization? What?
https://towardsdatascience.com/stemming-lemmatization-what-ba782b7c0bd8

[NLP] Text Processing Via Stemming And Lemmatisation In Data Science Projects
https://medium.com/fintechexplained/nlp-text-processing-via-stemming-and-lemmatisation-in-data-science-projects-ad4d5176060e

[NLP] NLP Guide: Identifying Part of Speech Tags using Conditional Random Fields
https://medium.com/analytics-vidhya/pos-tagging-using-conditional-random-fields-92077e5eaa31

[NLP] Evoking Syntax: Part 1, Part-of-Speech
https://medium.com/forge-ai/evoking-syntax-part-1-part-of-speech-69e0997bf25e

[NLP] An Introduction to Text Summarization using the TextRank Algorithm (with Python implementation)
https://medium.com/analytics-vidhya/an-introduction-to-text-summarization-using-the-textrank-algorithm-with-python-implementation-2370c39d0c60

[NLP] Text Classification: Applications and Use Cases
https://towardsdatascience.com/text-classification-applications-and-use-cases-beab4bfe2e62

[NLP] Building a Simple Chatbot from Scratch in Python (using NLTK)
https://medium.com/analytics-vidhya/building-a-simple-chatbot-in-python-using-nltk-7c8c8215ac6e

[NLP] Unsupervised Text Summarization using Sentence Embeddings
https://medium.com/jatana/unsupervised-text-summarization-using-sentence-embeddings-adb15ce83db1

[NLP] Understanding Word Embeddings with TF-IDF and GloVe
https://towardsdatascience.com/understanding-word-embeddings-with-tf-idf-and-glove-8acb63892032

[NLP] A Brief History of Word Embeddings
https://www.gavagai.io/text-analytics/a-brief-history-of-word-embeddings/

[NLP] Word Embeddings in NLP and its Applications
https://medium.com/aimarketingassociation/word-embeddings-in-nlp-and-its-applications-5fc147950777

(NLP) Tanimoto VS. Mol2vec (Molecules can be translated into vectors too!)
https://medium.com/gsi-technology/tanimoto-vs-mol2vec-7fa4af3208ef

(NLP) Building a Question Answering model
https://towardsdatascience.com/nlp-building-a-question-answering-model-ed0529a68c54

[NLP] Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python
https://medium.com/analytics-vidhya/tutorial-on-text-classification-nlp-using-ulmfit-and-fastai-library-in-python-2f15a2aac065

[NLP] Using Word2Vec for Better Embeddings of Categorical Features
https://towardsdatascience.com/using-word2vec-for-better-embeddings-of-categorical-features-de75020e1233

[NLP] Word2Vec -Negative Sampling made easy
https://medium.com/towardsdatascience/word2vec-negative-sampling-made-easy-7a1a647e07a4

[NLP] Deep Learning for Natural Language Processing Using word2vec-keras
https://towardsdatascience.com/deep-learning-for-natural-language-processing-using-word2vec-keras-d9a240c7bb9d

[NLP] Topic Modelling in Python with NLTK and Gensim
https://towardsdatascience.com/topic-modelling-in-python-with-nltk-and-gensim-4ef03213cd21

[NLP] Topic Modeling and Latent Dirichlet Allocation (LDA) in Python
https://towardsdatascience.com/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24

[NLP] 5 Text Classification Case Studies Using SciKit Learn
https://www.datasciencecentral.com/profiles/blogs/5-text-classification-case-studies-using-scikit-learn

[NLP] Introduction to Named Entity Recognition (NER)
https://medium.com/explore-artificial-intelligence/introduction-to-named-entity-recognition-eda8c97c2db1

[NLP] Named Entity Recognition and Classification for Entity Extraction
https://medium.com/district-data-labs/named-entity-recognition-and-classification-for-entity-extraction-6f23342aa7c5

[NLP] Text Classification Using LSTM and visualize Word Embeddings: Part-1
https://medium.com/@sabber/classifying-yelp-review-comments-using-lstm-and-word-embeddings-part-1-eb2275e4066b

[NLP] Text Generation using Bidirectional LSTM and Doc2Vec models 2/3
https://medium.com/@david.campion/text-generation-using-bidirectional-lstm-and-doc2vec-models-2-3-f0fc07ee7b30

[NLP] Precision And Recall — How It’s Used in Deep Learning Predictions
https://medium.com/@rossbulat/precision-and-recall-how-its-used-in-deep-learning-a252d03ed792

[NLP] How to Use ROC Curves and Precision-Recall Curves for Classification in Python
https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/

[NLP] 7 Things You Should Know about ROC AUC
https://medium.com/hiredscore-engineering/7-things-you-should-know-about-roc-auc-b4389ea2b2e3

[NLP] These are the Easiest Data Augmentation Techniques in Natural Language Processing you can think of — and they work.
https://towardsdatascience.com/these-are-the-easiest-data-augmentation-techniques-in-natural-language-processing-you-can-think-of-88e393fd610

[NLP] The Essential NLP Guide for data scientists (with codes for top 10 common NLP tasks)
https://www.analyticsvidhya.com/blog/2017/10/essential-nlp-guide-data-scientists-top-10-nlp-tasks/

[NLP] See this simple introduction to Natural Language Processing (NLP)
https://www.datasciencecentral.com/profiles/blogs/see-this-simple-introduction-to-natural-language-processing-nlp

[NLP] Best Practices for Text Classification with Deep Learning
https://machinelearningmastery.com/best-practices-document-classification-deep-learning/

[NLP] Using word2vec to Analyze News Headlines and Predict Article Success
https://towardsdatascience.com/using-word2vec-to-analyze-news-headlines-and-predict-article-success-cdeda5f14751

[NLP] Performance of Different Word Embeddings on Text Classification
https://towardsdatascience.com/nlp-performance-of-different-word-embeddings-on-text-classification-de648c6262b

[NLP] NLTK Applications for NLP and Python
https://towardsdatascience.com/nltk-applications-for-nlp-and-python-dc8c5381668a

[NLP] Using Word2vec for Music Recommendations
https://towardsdatascience.com/using-word2vec-for-music-recommendations-bb9649ac2484

[NLP] Game of Thrones Word Embeddings, does R+L = J ? — part 1
https://towardsdatascience.com/game-of-thrones-word-embeddings-does-r-l-j-part-1-8ca70a8f1fad

[NLP] Scikit-Learn for Text Analysis of Amazon Fine Food Reviews
https://towardsdatascience.com/scikit-learn-for-text-analysis-of-amazon-fine-food-reviews-ea3b232c2c1b

[NLP] Training and Visualising Word Vectors
https://towardsdatascience.com/training-and-visualising-word-vectors-2f946c6430f8

[NLP] Text Summarization using Deep Learning
https://towardsdatascience.com/text-summarization-using-deep-learning-6e379ed2e89c

[NLP] Neural Machine Translation — Using seq2seq with Keras
https://towardsdatascience.com/neural-machine-translation-using-seq2seq-with-keras-c23540453c74

[NLP] Probability concepts explained: Bayesian inference for parameter estimation
https://towardsdatascience.com/probability-concepts-explained-bayesian-inference-for-parameter-estimation-90e8930e5348

[NLP] Another Twitter sentiment analysis with Python — Part 9 (Neural Networks with Tfidf vectors using Keras)
https://towardsdatascience.com/another-twitter-sentiment-analysis-with-python-part-9-neural-networks-with-tfidf-vectors-using-d0b4af6be6d7

[NLP] Seq2seq for NLP: encoder-decoder framework for Tensorflow
https://www.datasciencecentral.com/profiles/blogs/seq2seq

[NLP] Multi-Class Text Classification with Scikit-Learn
https://towardsdatascience.com/multi-class-text-classification-with-scikit-learn-12f1e60e0a9f

[NLP] Multi-Class Text Classification with PySpark
https://towardsdatascience.com/multi-class-text-classification-with-pyspark-7d78d022ed35

[NLP] Understanding how Convolutional Neural Network (CNN) perform text classification with word embeddings
https://towardsdatascience.com/understanding-how-convolutional-neural-network-cnn-perform-text-classification-with-word-d2ee64b9dd0b

[NLP] Automated Text Classification Using Machine Learning
https://www.datasciencecentral.com/profiles/blogs/automated-text-classification-using-machine-learning

[NLP] A Simple Guide On Using BERT for Binary Text Classification.
https://medium.com/swlh/a-simple-guide-on-using-bert-for-text-classification-bbf041ac8d04

[NLP] Dissecting BERT Part 1: The Encoder
https://medium.com/dissecting-bert/dissecting-bert-part-1-d3c3d495cdb3

[NLP] BERT for dummies — Step by Step Tutorial
https://towardsdatascience.com/bert-for-dummies-step-by-step-tutorial-fb90890ffe03

[NLP] BLEU-BERT-y: Comparing sentence scores
https://towardsdatascience.com/bleu-bert-y-comparing-sentence-scores-307e0975994d

[NLP] NER with BERT in Action
https://medium.com/@yingbiao/ner-with-bert-in-action-936ff275bc73

ELMo Embedding — The Entire Intent of a Query
https://medium.com/analytics-vidhya/elmo-embedding-the-entire-intent-of-a-query-530b268c4cd

ELMo: Contextual language embedding
https://towardsdatascience.com/elmo-contextual-language-embedding-335de2268604

[NLP] Information Retrieval document search using vector space model in R
http://www.dataperspective.info/2017/11/information-retrieval-document-search-using-vector-space-model-in-r.html

[NLP] Sentiment Analysis of Movie Reviews (1):Bag-of-Words Models
https://www.datasciencecentral.com/profiles/blogs/test

[NLP] An introduction to Bag of Words and how to code it in Python for NLP
https://medium.com/free-code-camp/an-introduction-to-bag-of-words-and-how-to-code-it-in-python-for-nlp-282e87a9da04

[NLP] Finding Similar Quora Questions with BOW, TFIDF and Xgboost
https://towardsdatascience.com/finding-similar-quora-questions-with-bow-tfidf-and-random-forest-c54ad88d1370

[NLP] Machine Learning with Python: NLP and Text Recognition
https://levelup.gitconnected.com/machine-learning-with-python-nlp-and-text-recognition-94444d55b0ef

[NLP] A Basic NLP Tutorial for News Multiclass Categorization
https://medium.com/@armandj.olivares/a-basic-nlp-tutorial-for-news-multiclass-categorization-82afa6d46aa5

[NLP] A Stepwise Introduction to Topic Modeling using Latent Semantic Analysis (using Python)
https://medium.com/analytics-vidhya/text-mining-101-a-stepwise-introduction-to-topic-modeling-using-latent-semantic-analysis-using-add9c905efd9

[NLP] VerbiAge: Using NLP to help writers craft age-specific writing
https://blog.insightdatascience.com/verbiage-using-nlp-to-improve-k-12-content-marketing-8906d2810fda

[NLP] A Must-Read NLP Tutorial on Neural Machine Translation — The Technique Powering Google Translate
https://medium.com/analytics-vidhya/a-must-read-nlp-tutorial-on-neural-machine-translation-the-technique-powering-google-translate-c5c8d97d7587

Simple Transformers — Multi-Class Text Classification with BERT, RoBERTa, XLNet, XLM, and DistilBERT
https://medium.com/swlh/simple-transformers-multi-class-text-classification-with-bert-roberta-xlnet-xlm-and-8b585000ce3a

Fine-Tune ERNIE 2.0 for Text Classification
https://towardsdatascience.com/https-medium-com-gaganmanku96-fine-tune-ernie-2-0-for-text-classification-6f32bee9bf3c

When Topic Modeling is Part of the Text Pre-processing (*)
https://towardsdatascience.com/when-topic-modeling-is-part-of-the-text-pre-processing-294b58d35514

All you need to know about text preprocessing for NLP and Machine Learning
https://towardsdatascience.com/all-you-need-to-know-about-text-preprocessing-for-nlp-and-machine-learning-bc1c5765ff67

[NLP] Natural Language Understanding with Sequence to Sequence Models
https://towardsdatascience.com/natural-language-understanding-with-sequence-to-sequence-models-e87d41ad258b

[NLP] Sentence Prediction Using a Word-level LSTM Text Generator — Language Modeling Using RNN
https://medium.com/towards-artificial-intelligence/sentence-prediction-using-word-level-lstm-text-generator-language-modeling-using-rnn-a80c4cda5b40

[NLP] Fix your text thought attention before NLP tasks
https://towardsdatascience.com/fix-your-text-thought-attention-before-nlp-tasks-7dc074b9744f

[NLP] Benchmarking Python NLP Tokenizers
https://towardsdatascience.com/benchmarking-python-nlp-tokenizers-3ac4735100c5

Métricas (Metrics)

The Best Metric to Measure Accuracy of Classification Models
https://www.datasciencecentral.com/profiles/blogs/the-best-metric-to-measure-accuracy-of-classification-models

AI/ML Security Pro Tips: Class Imbalance and Missing Labels
https://medium.com/ai-ml-at-symantec/ai-ml-security-pro-tips-class-imbalance-and-missing-labels-764fd18b7bf8

Comparing Different Classification Machine Learning Models for an imbalanced dataset
https://towardsdatascience.com/comparing-different-classification-machine-learning-models-for-an-imbalanced-dataset-fdae1af3677f

How and When to Use a Calibrated Classification Model with scikit-learn
https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/

Overview of Text Similarity Metrics in Python
https://towardsdatascience.com/overview-of-text-similarity-metrics-3397c4601f50

Accuracy Trap! Pay Attention to Recall, Precision, F-Score, AUC
https://medium.com/datadriveninvestor/accuracy-trap-pay-attention-to-recall-precision-f-score-auc-d02f28d3299c

Hyperparameter Tuning
https://towardsdatascience.com/hyperparameter-tuning-c5619e7e6624

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