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Kaggle is an AirBnB for Data Scientiststhis is where they spend their nights and
Zeeshan-ul-hassan Usmani
https://towardsdatascience.com/how-to-compete-for-zillow-prize-at-kaggle-535852243906 | How to Compete for Zillow Prize at Kaggle | [
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Introduction
Gowtham Dongari
https://towardsdatascience.com/how-to-create-new-features-using-clustering-4ae772387290 | How to create New Features using Clustering!! | [
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Three different ways
Francesco Zuppichini
https://towardsdatascience.com/how-to-create-word-embedding-in-tensorflow-ed0a61507dd0 | How to create Words Embedding in TensorFlow | [
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Analytics and data science are important tools in healthcare. Not just because they might be able to predict when someone will have a heart attack. Good data science and analytics are important tools because they can help make
SeattleDataGuy
https://towardsdatascience.com/how-to-develop-a-successful-healthcare-analytic... | How To Develop A Successful Healthcare Analytics Product | [
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Docker explained through Legos
Venkat Raman
https://towardsdatascience.com/how-to-dockerize-an-r-shiny-app-part-1-d4267659312a | How to Dockerize an R Shiny AppPart 1 | [
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A guide on how to train a model to solve Wheres Wally puzzles
Tadej Magajna
https://towardsdatascience.com/how-to-find-wally-neural-network-eddbb20b0b90 | How to Find Wally with a Neural Network | [
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An introduction to creating music using
Sigur ur Sk li
https://towardsdatascience.com/how-to-generate-music-using-a-lstm-neural-network-in-keras-68786834d4c5 | How to Generate Music using a LSTM Neural Network in Keras | [
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Remote work is offered by a large part of software companies nowadays. However, this has
Dominic Monn
https://towardsdatascience.com/how-to-get-a-remote-machine-learning-job-aa378d9879f9 | How to get a Remote Machine Learning Job | [
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Several friends have found my story useful for their own job search and getting into Data Science. Figured
Rikin Mathur
https://towardsdatascience.com/how-to-get-into-data-science-ca61930360c8 | How to get into Data Science | [
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Estimating the claps you get, the data science way
Neerja Doshi
https://towardsdatascience.com/how-to-get-more-likes-on-your-blogs-2-2-f8ef0be21771 | How to get more likes on your blogs (2/2) | [
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PySpark is a Python API to using Spark, which is a parallel and distributed engine for running big data applications. Getting started with PySpark took me a few hourswhen it shouldnt haveas I had to read a lot of blogs/documentation to debug some of the setup issues
Sanket Gupta
https://towardsdatascience.com/how-to-ge... | How to Get Started with PySpark | [
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An example of why the most important skills in data
William Koehrsen
https://towardsdatascience.com/how-to-get-the-right-data-why-not-ask-for-it-d26ced1bbd46 | How to get the right data? Trying asking for it. | [
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And Some Resources I Love
Deepal Dsilva
https://towardsdatascience.com/how-to-identify-the-best-resources-to-be-a-self-taught-data-scientist-33f954f67f69 | How To Identify The Best Resources To Be A Self-Taught Data Scientist | [
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Strategies to choose from to
Kritika Jalan
https://towardsdatascience.com/how-to-improve-my-ml-algorithm-lessons-from-andrew-ngs-experience-i-551ca1a32634 | How to Improve my ML Algorithm? Lessons from Andrew Ngs experienceI | [
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Strategies to choose from to
Kritika Jalan
https://towardsdatascience.com/how-to-improve-my-ml-algorithm-lessons-from-andrew-ngs-experience-ii-f66926926f88 | How to Improve my ML Algorithm? Lessons from Andrew Ngs experienceII | [
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Neural networks are machine learning algorithms that provide state of the
Rohith Gandhi
https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-9f5d1c6f407d | Improving the Performance of a Neural Network | [
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It is quite possible to learn, follow and contribute to state-of-art work in deep learning
Bargava
https://towardsdatascience.com/how-to-learn-deep-learning-in-6-months-e45e40ef7d48 | How to learn Deep Learning in 6 months | [
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Part 1: Table Stakes
Ian Blumenfeld
https://towardsdatascience.com/how-to-level-up-as-a-data-scientist-part-1-9ea6a775f239 | How to Level Up as a Data Scientist (Part 1) | [
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In Part 1 of this series I identified three areas I consider foundational skills
Ian Blumenfeld
https://towardsdatascience.com/how-to-level-up-as-a-data-scientist-part-2-92eb65aaf1c5 | How to Level Up as a Data Scientist (Part 2) | [
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For any digital or online business, winning customers is only one half of the battle. Its also about engaging, and ultimately retaining, customers to achieve long-term success. Solving the issue of customer churn has been one of
Dan Schoenbaum
https://towardsdatascience.com/how-to-leverage-ai-to-predict-and-prevent-cus... | How to Leverage AI to Predict (and Prevent) Customer Churn | [
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Recently I read the book How to lie with statistics by Darrel Huff. The book talks about how one can use
Dima Shulga
https://towardsdatascience.com/how-to-lie-with-data-science-5090f3891d9c | How to lie with Data Science | [
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Using Machine Learning algorithms, Amazon Alexa and DeviceHive
DeviceHive
https://towardsdatascience.com/how-to-make-devices-smart-10dff00c2a8c | How to make devices smart | [
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Why trying to avoid spreadsheets is the best way to learn data science
William Koehrsen
https://towardsdatascience.com/how-to-master-new-skills-656d42d0e09c | How to Master New Skills | [
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TL;DR
Chris Rawles
https://towardsdatascience.com/how-to-normalize-features-in-tensorflow-5b7b0e3a4177 | How to normalize features in TensorFlow | [
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How do I get into Data Science/ AI?
Rico Meinl
https://towardsdatascience.com/how-to-not-get-into-data-science-7b06a9947907 | How to NOT get into Data Science | [
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Time for an update on my One-Shot learning approach using a Siamese LSTM-based Deep Neural
Pascal Potvin
https://towardsdatascience.com/how-to-potty-train-a-siamese-network-3df6ca5e44da | How to potty train a Siamese Network | [
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I have been asked many times by clients to provide fixed price estimates for large Machine Learning (ML) projects. This is really tricky. Requirements often change midway through a project as a result of feature creep, development slippage, integration headaches, user acceptance
Daniel Shapiro, PhD
https://towardsdatas... | How to Price an AI Project | [
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using Paperspace and fast.ai
Thomas Filaire
https://towardsdatascience.com/how-to-set-up-a-powerful-and-cost-efficient-gpu-server-for-deep-learning-aa1de0d4ea56 | How to set-up a powerful and cost-efficient GPU server for deep learning | [
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Artificial Intelligence makes it possible to analyse images. In this blogpost I will focus on training a object detector with customized classes. The first thing you will have to do is the setup. In the Tensorflow documentation is
Dion van Velde
https://towardsdatascience.com/how-to-train-a-tensorflow-face-object-detec... | How to train a Tensorflow face object detection model | [
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A step-by-step guide using small and efficient neural
Norman Di Palo
https://towardsdatascience.com/how-to-train-your-self-driving-car-to-steer-68c3d24bbcb7 | How to Train your Self-Driving Car to Steer | [
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This is a quick guide that anybody can follow on training a machine learning model to detect open-ended questions in text.
Aaron Edell
https://towardsdatascience.com/how-to-use-ai-to-detect-open-ended-questions-for-non-datascientists-e2ef02427422 | How to use AI to detect open-ended questions for non-datascientists | [
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The built-in Input Pipeline. Never use feed-dict anymore
Francesco Zuppichini
https://towardsdatascience.com/how-to-use-dataset-in-tensorflow-c758ef9e4428 | How to use Dataset in TensorFlow | [
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In this post I will show how one can use natural language processing to extract keywords (aspects) from a product review. The idea is to essentially try to replicate what Amazon does with its reviews. For example in
Gunnvant Saini
https://towardsdatascience.com/how-to-use-natural-language-processing-to-analyze-product-... | How to use Natural Language Processing to analyze product reviews? | [
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Ive found that is a little difficult to get started with Apache Spark (this will focus on PySpark) on your local machine for most people. With this simple tutorial
Favio V zquez
https://towardsdatascience.com/how-to-use-pyspark-on-your-computer-9c7180075617 | How to use PySpark on your computer | [
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Learn about the popular puzzle called Sudoku and how we can teach computers to solve it on their own.
Grant Bartel
https://towardsdatascience.com/how-to-win-sudoku-3a82d05a57d | How To Win Sudoku | [
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A behind-the-scenes look at our data visualization process, plus how
Sohan Murthy
https://towardsdatascience.com/how-we-visualize-data-at-amino-c38e1ee4ba05 | How we visualize data at Amino | [
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The real rules about old and young you can date
Late Night Froyo
https://towardsdatascience.com/how-young-is-too-young-to-date-ae0061bc2115 | How Young is Too Young to Date? | [
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This is the first paper in a Seminal Papers in ML series by MIT Machine Intelligence Community (MIC). MIT MIC aims to educate the community at-large about machine learning and lower the barriers to entry. To learn more, please visit https://mitmic.io or email
Moin Nadeem
https://towardsdatascience.com/how-youtube-recom... | How YouTube Recommends Videos | [
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Is that idea is going to take you to the moon? Or is it destined to never leave the launchpad? Knowing if an idea is good or bad is really hard. However, dont despair! With the right tools, you can realize the full potential of your ideas.
Chris Fuller
https://towardsdatascience.com/https-medium-com-how-to-realize-the-... | How to realize the full potential of your ideas? | [
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You tasted tensorflow
Jan Zawadzki
https://towardsdatascience.com/https-medium-com-janzawadzki-sweet-or-cheat-build-a-sneaker-rater-after-finishing-andrew-ngs-2nd-course-49475fc75429 | Sweet or Cheat? Build a Sneaker Rater after finishing Andrew Ngs 2nd Course | [
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The European unicorn applies innovative structures and methods
Jan Zawadzki
https://towardsdatascience.com/https-medium-com-janzawadzki-working-as-a-data-scientist-at-scout24-48b15286e1a | Working as a Data Scientist at Scout24 | [
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Machine learning involves predicting and classifying data and to do so we employ
Rushikesh Pupale
https://towardsdatascience.com/https-medium-com-pupalerushikesh-svm-f4b42800e989 | Support Vector Machines(SVM)An Overview | [
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I was a huge Taylor Swift fan growing up. My middle school iPod Nanos top played songs are from Speak Now. I remember staying up late browsing Tumblr during my sophomore year while my parents were asleep, trying to find samples of Red
Shreya Shankar
https://towardsdatascience.com/https-medium-com-sh-reya-taylor-swift-o... | Taylor Swift vs Artificial Intelligence: Whos Better? | [
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How blockchain technology will help us create and use a single
Shaan Ray
https://towardsdatascience.com/https-medium-com-shaanray-how-blockchains-will-solve-privacy-88944f3c67f0 | Blockchains and Digital Identity | [
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What I learnt when applying Machine
Simon Kuttruf
https://towardsdatascience.com/https-medium-com-skuttruf-machine-learning-in-finance-algorithmic-trading-on-energy-markets-cb68f7471475 | A Machine Learning framework for an algorithmic trading system | [
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Why we musnt give up on climate change mitigation and an analysis of how its going so far.
Stephanie Willis
https://towardsdatascience.com/https-medium-com-stephaniewillis808-concerning-climate-5a6b923eb8eb | Concerning Climate | [
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There isnt a day in our lives when we dont use Google and the term googling has crept into our
Ansh Balde
https://towardsdatascience.com/https-towardsdatascience-com-search-engines-and-neural-networks-97e0df4f088d | Search Engines and Neural Networks | [
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Applying neural networks in real-time audio signal processing
Daniel Rothmann
https://towardsdatascience.com/human-like-machine-hearing-with-ai-1-3-a5713af6e2f8 | Human-Like Machine Hearing With AI (1/3) | [
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Or how my robot automagically refines a grasp shown by a human.
Ugo Cupcic
https://towardsdatascience.com/humans-are-handy-to-have-around-4e6b7a7acff7 | Humans are handy to have around | [
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The addition of working memory to artificial neural networks (ANNs) is an obvious upgrade when we compare ANNs to the Von Neumann CPU architecture, and one that came to the fore in
Humphrey Sheil
https://towardsdatascience.com/humphrey-sheil-differentiable-neural-computers-dncs-nature-article-thoughts-bd22939c2d97 | Differentiable Neural Computers (DNCs)Nature article thoughts | [
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Deep Learning enjoys a massive hype at the moment. People want to use Neural Networks everywhere, but
Niklas Donges
https://towardsdatascience.com/hype-disadvantages-of-neural-networks-6af04904ba5b | Pros and Cons of Neural Networks | [
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Part IActivation Functions
Daniel Godoy
https://towardsdatascience.com/hyper-parameters-in-action-a524bf5bf1c | Hyper-parameters in action! | [
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In this post, I will show you the importance
Daniel Godoy
https://towardsdatascience.com/hyper-parameters-in-action-part-ii-weight-initializers-35aee1a28404 | Hyper-parameters in Action! Part II-Weight Initializers | [
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Finding the right hyperparameters for your deep learning model can be a tedious process. It doesnt have to.
Mikko
https://towardsdatascience.com/hyperparameter-optimization-with-keras-b82e6364ca53 | Hyperparameter Optimization with Keras | [
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One way to deal with the wait for Infinity Wars is to play with some Avengers data. I took the scripts for the last three Marvel Cinematic Universe crossover films (The Avengers, Avengers: Age of Ultron, and
Elle O'Brien
https://towardsdatascience.com/i-analyzed-marvel-movie-scripts-to-learn-what-each-avenger-says-most... | I analyzed Marvel movie scripts to learn what each Avenger says most. | [
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Well, what you hate is the way that math was taught to you. Would you give it
Pablo Casas
https://towardsdatascience.com/i-hate-math-part-1-4e793f5a8f72 | [
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A week or so I gave a talk a talk on support vector machines at General Assembly
Dale Wahl
https://towardsdatascience.com/i-support-vector-machines-and-so-should-you-7af122b6748 | I support vector machines and so should you. | [
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This years International Conference on Learning Representations (ICLR) was held in beautiful
Aerin Kim
https://towardsdatascience.com/iclr-2018-posters-highlight-part-1-386e674f2f9a | ICLR 2018 Posters Highlight (part 1) | [
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Using data science to understand movements in
Chaim Gluck
https://towardsdatascience.com/identifying-when-poems-were-written-with-natural-language-processing-a40ff286bcd | When was this poem written? My computer can tell you | [
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Machine learning is the new hotness, we all agree. Probably every story youve read with a click-baity title like 7 Technology Trends That Will Dominate 2018 will mention machine learning or AI in one way or another.
Aaron Edell
https://towardsdatascience.com/if-machine-learning-isnt-saving-you-money-you-re-doing-it-wro... | If machine learning isnt saving you money, youre doing it wrong | [
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How to avert computer
William Koehrsen
https://towardsdatascience.com/if-your-files-are-saved-only-on-your-laptop-they-might-as-well-not-exist-29f3503750d5 | If your files are saved only on your laptop they might as well not exist! | [
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Andrew Ngs Convolutional Neural Network course review
Pascal Potvin
https://towardsdatascience.com/ima-mag-age-7bc81399b0a6 | IMA MAG AGE | [
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What is Image Captioning?
Pranoy Radhakrishnan
https://towardsdatascience.com/image-captioning-in-deep-learning-9cd23fb4d8d2 | Image Captioning in Deep Learning | [
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In A.I., data is power.
Daniel Shapiro, PhD
https://towardsdatascience.com/image-datasets-for-artificial-intelligence-bbb12615edd7 | Image Datasets for Artificial Intelligence | [
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Weve been looking at how to use Scikit-learn with TensorFlow and in general discussing TensorFlows high-level API. Another popular high-level API that TensorFlow directly supports is Keras.
Karthik M Swamy
https://towardsdatascience.com/image-tagging-with-keras-in-tensorflow-1-2-bc43c1058019 | Image Tagging with Keras in TensorFlow 1.3 | [
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A walkthrough of scikit-learns KNeighbors Classifier
Chaim Gluck
https://towardsdatascience.com/implementing-k-nearest-neighbors-with-scikit-learn-9e4858e231ea | Implementing K-Nearest Neighbors in scikit-learn | [
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With this Python cheat sheet, youll have a handy reference guide to importing your data, from flat files to files native to other software, and relational databases.
Karlijn Willems
https://towardsdatascience.com/importing-data-in-python-cheat-sheet-712ba3638c78 | Importing Data in Python Cheat Sheet | [
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Contributors: Feiko Lai, Michal Szczecinski, Winnie So
Kamil Bujel
https://towardsdatascience.com/improving-operations-with-route-optimization-4b8a3701ca39 | Improving Operations with Route Optimization | [
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Gathering More Data and Feature Engineering
William Koehrsen
https://towardsdatascience.com/improving-random-forest-in-python-part-1-893916666cd | Improving the Random Forest in Python Part 1 | [
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Performance improvements applied to training neural networks
Devin Soni
https://towardsdatascience.com/improving-vanilla-gradient-descent-f9d91031ab1d | Improving Vanilla Gradient Descent | [
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What is a Markov chain?
Rohan Joseph
https://towardsdatascience.com/in-5-mins-the-markov-mouse-a4f7a38289fb | The Markov Mouse | [
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My notes from the PAPIs.io 2018 conference in London
Neal Lathia
https://towardsdatascience.com/industry-stories-about-machine-learning-bb5652455fe2 | Industry Stories about Machine Learning | [
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Widening inequalities of income and wealth in our modern society have started to attract the attention
nar Baymul
https://towardsdatascience.com/inequality-in-the-premier-league-a3a9a7294d96 | Inequality in the Premier League | [
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Although popular statistics libraries like SciPy and PyMC3 have pre-defined functions to compute different
Amulya Aankul
https://towardsdatascience.com/inferential-statistics-series-t-test-using-numpy-2718f8f9bf2f | T-test using Python and Numpy | [
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Data Science at work to find ISIS Extremists and
Marco Brambilla
https://towardsdatascience.com/influence-models-of-radicalization-on-social-media-a762fbc35c36 | Influence Models of Radicalization on Social Media | [
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Learning better with fewer examples
Vadim Smolyakov
https://towardsdatascience.com/information-planning-and-naive-bayes-380ee1feedc7 | Information Planning and Naive Bayes | [
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Opening the Black Box . Somewhat
Mukul Malik
https://towardsdatascience.com/information-theory-of-neural-networks-ad4053f8e177 | Information Theory of Neural Networks | [
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Integer Programming (IP) problems are optimization problems where all of the variables are constrained to
Freddy Boulton
https://towardsdatascience.com/integer-programming-in-python-1cbdfa240df2 | Integer Programming in Python | [
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Unlock the pathway to data-driven visualizations
DipanjanDJ) Sarkar
https://towardsdatascience.com/interactive-data-visualization-with-d3-js-43fc3428a27e | Interactive Data Visualization with D3.js | [
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Code notebooks come alive with interactive
Tirthajyoti Sarkar
https://towardsdatascience.com/interactive-machine-learning-make-python-lively-again-a96aec7e1627 | Interactive Machine Learning: Make Python Lively Again | [
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This entry is a non-exhaustive introduction on how to create interactive content directly from your Jupyter notebook. Content mostly refers to data visualization artifacts, but well see that we can easily expand beyond the usual plots and graphs
5agado
https://towardsdatascience.com/interactive-visualizations-in-jupyte... | Interactive Visualizations In Jupyter Notebook | [
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This is another off-the-beaten-path problem, one that you wont find in textbooks. You can solve it using data science methods (my approach) but the mathematician with some spare time could find an elegant solution. Share it
Vincent Granville
https://towardsdatascience.com/interesting-probability-problem-self-correcting... | Interesting Probability Problem: Self-correcting Random Walks | [
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Regardless of the end goal of your data science solutions, an end-user will always prefer
Lars Hulstaert
https://towardsdatascience.com/interpretability-in-machine-learning-70c30694a05f | Interpreting machine learning models | [
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This is a story about the danger of interpreting your machine learning model
Scott Lundberg
https://towardsdatascience.com/interpretable-machine-learning-with-xgboost-9ec80d148d27 | Interpretable Machine Learning with XGBoost | [
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Going zero to not-quite-hero in NLP via hate speech classification
Leon Zhou
https://towardsdatascience.com/into-a-textual-heart-of-darkness-39b3895ce21e | Into a Textual Heart of Darkness | [
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Data analysis is part of any data scientists daily work (along with data munging and
SeattleDataGuy
https://towardsdatascience.com/intro-to-data-analysis-for-everyone-part-1-ff252c3a38b5 | Intro To Data Analysis For Everyone! Part 1 | [
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We hope you have enjoyed both previous parts(part 1, part 2) of this intro to data
SeattleDataGuy
https://towardsdatascience.com/intro-to-data-analysis-for-everyone-part-3-d8f02690fba0 | Intro To Data Analysis For Everyone; Part 3! | [
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Descriptive Statistical Analysis helps you to understand your data and is a very important part of
Niklas Donges
https://towardsdatascience.com/intro-to-descriptive-statistics-252e9c464ac9 | Intro to Descriptive Statistics | [
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Introducing a simple
Tirthajyoti Sarkar
https://towardsdatascience.com/introducing-a-simple-and-intuitive-python-api-for-uci-machine-learning-repository-fd2ce8eb6cd4 | Introducing a simple and intuitive Python API for UCI machine learning repository | [
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A lightweight Python package for
Tirthajyoti Sarkar
https://towardsdatascience.com/introducing-pydbgen-a-random-dataframe-database-table-generator-b5c7bdc84be5 | Introducing pydbgen: A random dataframe/database table generator | [
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A measure of startup competition.
Sebastian Quintero
https://towardsdatascience.com/introducing-the-capital-concentration-index-a407fcd3da2b | Introducing the Capital Concentration Index | [
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An explanation of the Bayesian approach to linear modeling
William Koehrsen
https://towardsdatascience.com/introduction-to-bayesian-linear-regression-e66e60791ea7 | Introduction to Bayesian Linear Regression | [
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Bayesian networks
Devin Soni
https://towardsdatascience.com/introduction-to-bayesian-networks-81031eeed94e | Introduction to Bayesian Networks | [
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Optimization by natural selection
Devin Soni
https://towardsdatascience.com/introduction-to-evolutionary-algorithms-a8594b484ac | Introduction to Evolutionary Algorithms | [
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Two-player Sequential gamesDominant Strategies, Nash
Devin Soni
https://towardsdatascience.com/introduction-to-game-theory-part-1-1a812d898e84 | Introduction to Game Theory (Part 1) | [
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What is k-Nearest-Neighbors (kNN), some useful applications, and
Devin Soni
https://towardsdatascience.com/introduction-to-k-nearest-neighbors-3b534bb11d26 | Introduction to k-Nearest-Neighbors | [
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On this episode of AI Adventures, find out what Kaggle Kernels are and how to get started using them
Yufeng G
https://towardsdatascience.com/introduction-to-kaggle-kernels-2ad754ebf77 | Introduction to Kaggle Kernels | [
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What are Markov chains, when to use them, and how they work
Devin Soni
https://towardsdatascience.com/introduction-to-markov-chains-50da3645a50d | Introduction to Markov Chains | [
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A Decision Tree is a powerful supervised learning tool in Machine Learning for splitting
Anson Wong
https://towardsdatascience.com/introduction-to-model-trees-6e396259379a | Introduction to Model Trees from scratch | [
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Naive Bayes
Devin Soni
https://towardsdatascience.com/introduction-to-naive-bayes-classification-4cffabb1ae54 | Introduction to Naive Bayes Classification | [
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0.16899019479751587,
-0.2203327715396881,
0.00988437421... |
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