Data Exploration & Machine Learning, Hands-on

Practical Walkthroughs on Machine Learning, Data Exploration and Insight Finding






Welcome to amunategui.github.io, your portal for practical data science walkthroughs in the Python and R programming languages


I attempt to break down complex machine learning ideas and algorithms into practical applications using clear steps and publicly available data sets. If you're looking for applied walkthroughs of ML and AI concepts, you've come to the right place - happy learning!



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  1. One Line of Code to Send Messages to a Discord Server

  2. Grow Your Web Brand, Visibility & Traffic Organically - 5 Years of amunategui.github.io

  3. The Python and Flask Rest API, Abstracting Functions for Web Applications and SaaS

  4. What they Didn’t Teach at Data Science School, and How to Fix It to 10x Your Career

  5. GDELT - World Events at Your Finger Tips and for Free!

  6. We Can All Be Internet Moguls — How to Create and Sell Your Machine Learning Product Online and For Free

  7. How to Create Your Own Free Email Signup Form and Enjoy 100% Creative Freedom - For Static & Semi-Static Web Sites

  8. From Financial Compliance to Fraud Detection with Conditional Variational Autoencoders (CVAE) and Tensorflow

  9. How Blogging and Making YouTube Videos Landed Me the Best Job

  10. Your Git Commit Comments, and What They Reveal About You

  11. Exploring Some Pair-Trading Concepts with Python

  12. My Six Favorite Free Data Science Classes and the Giants Behind Them

  13. Hosting a Flask Application on AWS Beanstalk

  14. TensorFlow Won the Attention Battle, Who’s Next?

  15. GPUs on Google Cloud - the Fast Way & the Slow Way

  16. Executive Time Management — Don’t Suffocate the Creative Process

  17. Pairing Reinforcement Learning and Machine Learning, an Enhanced Emergency Response Scenario

  18. Find Your Next Programming Language By Measuring “The Knowledge Gap” on StackOverflow.com

  19. My #1 Piece of Advice for Aspiring Data Scientists

  20. Chatbot Conversations From Customer Service Transcripts

  21. Serverless Hosting On Microsoft Azure - A Simple Flask Example

  22. Google Video Intelligence, TensorFlow And Inception V3 - Recognizing Not-So-Famous-People

  23. Rapid Prototyping on Google App Engine - Build a Trip Planner with Google Maps and Yelp

  24. Yelp v3 and a Romantic Trip Across the USA, One Florist at a Time

  25. Show it to the World! Build a Free Art Portfolio Website on GitHub.io in 20 Minutes!

  26. Google Video Intelligence and Vision APIs - Automatically Recognize Actors and Download their Biographies in Real Time

  27. Life Coefficients - Modeling Life Expectancy and Prototyping it on the Web with Flask and PythonAnywhere

  28. Convolutional Neural Networks And Unconventional Data - Predicting The Stock Market Using Images

  29. The Fallacy of the Data Scientist's Venn Diagram

  30. Reinforcement Learning - A Simple Python Example and a Step Closer to AI with Assisted Q-Learning

  31. Simple Heuristics - Graphviz and Decision Trees to Quickly Find Patterns in your Data

  32. Office Automation Part 3 - Classifying Enron Emails with Google's Tensorflow Deep Neural Network Classifier

  33. Office Automation Part 2 - Using Pre-Trained Word-Embedded Vectors to Categorize the Enron Email Dataset

  34. Office Automation Part 1 - Sorting Departmental Emails with Tensorflow and Word-Embedded Vectors

  35. Easy Market Profile in Python: Grasp Price Action Quickly

  36. What-if Roadmap - Assessing Live Opportunities and their Paths to Success or Failure

  37. Where Are Your Customers Coming From And Where Are They Going - Reporting On Complex Customer Behavior In Plain English With C5.0

  38. Databricks, SparkR and Distributed Naive Bayes Modeling

  39. R and Azure ML - Your One-Stop Modeling Pipeline in The Cloud!

  40. Get Your "all-else-held-equal" Odds-Ratio Story for Non-Linear Models!

  41. Predict Stock-Market Behavior using Markov Chains and R

  42. Big Data Surveillance: Use EC2, PostgreSQL and Python to Download all Hacker News Data!

  43. The Peter Norvig Magic Spell Checker in R

  44. Actionable Insights: Getting Variable Importance at the Prediction Level in R

  45. Survival Ensembles: Survival Plus Classification for Improved Time-Based Predictions in R

  46. Anomaly Detection: Increasing Classification Accuracy with H2O's Autoencoder and R

  47. H2O & RStudio Server on Amazon Web Services (AWS), the Easy Way!

  48. Analyze Classic Works of Literature from Around the World with Project Gutenberg and R

  49. Speak Like a Doctor - Use Natural Language Processing to Predict Medical Words in R

  50. Supercharge R with Spark: Getting Apache's SparkR Up and Running on Amazon Web Services (AWS)

  51. R and Excel: Making Your Data Dumps Pretty with XLConnect

  52. Going from an Idea to a Pitch: Hosting your Python Application using Flask and Amazon Web Services (AWS)

  53. Getting PubMed Medical Text with R and Package {RISmed}

  54. Find Variable Importance for any Model - Prediction Shuffling with R

  55. Bagging / Bootstrap Aggregation with R

  56. Feature Hashing (a.k.a. The Hashing Trick) With R

  57. Yelp, httr and a Romantic Trip Across the United States, One Florist at a Time

  58. Quantifying the Spread: Measuring Strength and Direction of Predictors with the Summary Function

  59. Downloading Data from Google Trends And Analyzing It With R

  60. Using String Distance {stringdist} To Handle Large Text Factors, Cluster Them Into Supersets

  61. SMOTE - Supersampling Rare Events in R

  62. Let's Get Rich! See how {quantmod} And R Can Enrich Your Knowledge Of The Financial Markets!

  63. How To Work With Files Too Large For A Computer’s RAM? Using R To Process Large Data In Chunks

  64. Predicting Multiple Discrete Values with Multinomials, Neural Networks and the {nnet} Package

  65. Modeling 101 - Predicting Binary Outcomes with R, gbm, glmnet, and {caret}

  66. Reducing High Dimensional Data with Principle Component Analysis (PCA) and prcomp

  67. The Sparse Matrix and {glmnet}

  68. Brief Walkthrough Of The dummyVars Function From {caret}

  69. Ensemble Feature Selection On Steroids: {fscaret} Package

  70. Mapping The United States Census With {ggmap}

  71. Using Correlations To Understand Your Data

  72. Brief Guide On Running RStudio Server On Amazon Web Services