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Learning Labs Pro
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Video Subtitles (Captions)
Natural Language Processing (NLP)
Introduction to Text Analysis with R | Resume Analyzer Shiny App (90:33)
Excel to R & Python
Risk Analysis & Simulation with Python | FB Adspend Excel Project (125:43)
Risk Analysis & Simulation with R | Shiny Monte Carlo Simulation App (119:16)
Spark in R & Python
Lab 67: Time Series with Spark (Modeltime) | Shiny Google Analytics Forecaster (113:55)
Lab 66: Spark in Python | PySpark | Dash App NASDAQ Stock Screener (98:08)
Lab 65: Spark in R | sparklyr | Shiny App NASDAQ Stock Screener (100:28)
Time Series: Python & R
Lab 64: How to Forecast 100 Time Series | Python Sktime (93:10)
Lab 63: How to Forecast 100 Time Series | R Modeltime
SPECIAL: Forecasting with Modeltime!
Lab 63: How to Forecast 100 Time Series | Nested Modeltime (113:01)
Lab 60: Airline Covid Forecasting | NEW Modeltime & Modeltime GluonTS Features (93:34)
Lab 54: Energy Demand Forecasting | Autoregression Modeltime Recursive (95:16)
Lab 53B: Modeltime H2O | Forecasting with H2O AutoML! (63:43)
Lab 53A: Modeltime GluonTS Deep Learning | Saturn Cloud GPUs (78:07)
Lab 50: Hierarchical Forecasting | Shiny Hierarchical Forecasting App Bonus
Lab 47: Forecasting with Autoregressive Machine Learning (Recursive) | Scalable AR(ML) Bonus (86:47)
Lab 46: Forecasting at Scale with Modeltime | Nostradamus Lite Shiny Bonus (75:15)
Lab 38: Time Series Forecasting | Intro to Modeltime (85:29)
Marketing Analytics with R & Python
Lab 62 (Python): Marketing Mix Modeling (MMM) Optimization | Dash App (72:33)
Lab 61 (R): Automated Marketing Mix Modeling (MMM) | Facebook Robyn | Shiny App (85:45)
Lab 59 (Python): Customer Lifetime Value (CLV | RFM) with Machine Learning | Dash CLV App (103:52)
Lab 58 (R): Customer Lifetime Value (CLV | RFM) with Machine Learning | Shiny CLV App (105:33)
Production Data Science Pipelines with Targets
Lab 57: Targets + Modeltime (ARIMA & Prophet) | Energy Forecasting Report Automation | Special Guest: Will Landau (Targets Creator) (122:47)
Lab 56: Targets Data Science Pipelines | Bonus: Shiny Customer Analytics App (99:58)
Tidymodels Ecosystem
Lab 55: Workflowsets | HR Compensation Modeling | Bonus: CEO Explorer App | Special Guest: Julia Silge (99:25)
Lab 52: Stacks Ensembles | Customer Churn Retention App (76:33)
Lab 51: Deep Learning with Torch & Tabnet | Shiny Loan Default Scorer App Bonus | Special Guest: Josh Starmer BAM! (91:34)
Lab 50: Hierarchical Forecasting | Shiny Hierarchical Forecast App Bonus (105:37)
Lab 49: Feature Engineering for Customer Analytics | Special Guest: MAX KUHN | Shiny Customer Explorer App (91:54)
Lab 48: NLP for Business | Text Recipes | AutoNLP Shiny App Bonus (88:14)
Building an R Package | R Package Developer Series
Lab 45: Shiny Apps with Golem | golem | Shiny PowerPoint Golem App Bonus (95:07)
Lab 44: R Package Development | usethis | Shiny PowerPoint Bonus (110:58)
Lab 43: Tidy PowerPoint Automation | officer & rlang | "Functionizing" Workflow (90:30)
R in Production | MLOps Series
Lab 42: Automating Google Sheets with R API (Plumber, Docker, & AWS) (86:12)
Lab 41: Forecasting at Scale with MetaFlow + Modeltime + AWS (97:21)
Lab 40: Docker for Data Science (91:37)
Lab 39: H2O & MLFlow for Bankruptcy Prediction API (88:47)
Python + R Series
Lab 37: NLP & PDF Text Extraction (spaCy) (100:37)
Lab 36: Tensorflow Multivariate Forecasting (Energy, LSTM) (108:17)
Lab 35: TensorFlow for Finance & Gold Price Forecaster App (Time Series, LSTM) (119:27)
Lab 34: Advanced Customer Segmentation & Market Basket App (E-Commerce) (107:21)
Lab 33: Employee Segmentation w/ Scikit-Learn (HR Analytics) (88:08)
Shiny API Series
Lab 32: Text Mining Tweets with Twitter & Tidytext (91:07)
Lab 31: Forecasting Google Analytics with Facebook Prophet & Shiny (79:26)
Lab 30: Shiny Finance with Tidyquant (Excel in R) (88:54)
Lab 29: Shiny Crude Oil Forecast (Multivariate ARIMA) App with Fable & Quandl API (83:13)
Lab 28: Shiny Real Estate App with Zillow API (72:50)
Marketing Analytics Series
Lab 27: Google Trends Automation with Shiny (66:52)
Lab 26: Machine Learning for Customer Journey (96:38)
Lab 25: Marketing Multi-Channel Attribution with ChannelAttribution (96:08)
Lab 24: A/B Testing for Website Optimization with Infer & Google Optimize (90:59)
Lab 14: Customer Churn Survival Analysis w/ correlationfunnel, parsnip, & H2O (88:30)
Lab 11: Market Basket Analysis & Recommendation Systems w/ recommenderlab (78:35)
Lab 3: Marketing Analytics Case Study - Excel to R (77:54)
Databases - SQL
Lab 23 - Google Analytics & BigQuery (SQL) - Conversion Funnel Analysis (85:04)
Lab 22 - SQL for Time Series - Stocks & Fannie Mae Mortgage Delinquency Analysis (90:16)
Lab 21 - SQL for Data Science - Home Loans with SQL, R, & dplyr (92:06)
Explainable Machine Learning
Lab 20 - Explaining Machine Learning for Customer Churn (79:03)
Network Analysis
Lab 19 - Using Customer Credit Card History to Cluster with Network Analysis (83:09)
Anomaly Detection
Lab 18 - Time Series Anomaly Detection - anomalize (87:15)
Lab 17 - Anomaly Detection with H2O Machine Learning (90:34)
Optimization & Simulation
Lab 16: R Optimization Toolchain - Part 2 - Stock Portfolio & Nonlinear Programming with ROI (88:09)
Lab 15: R Optimization Toolchain - Part 1 - Product Mix & Linear Programming with ompr (80:35)
Big Data
Lab 13: Wrangling 4.6M Rows (375 MB) of Financial Data with data.table (78:36)
Time Series
Lab 7: 5 Strategies to Improve Business Forecasting by 50% (or more) (89:02)
Production: Shiny & Plumber
Lab 10: Building API's with Plumber & Postman (80:18)
Data Collection
Lab 8: Web Scraping - Build A Strategic Database With Product Data (70:07)
Domain: Finance
Lab 9: Finance with R - Performance Analysis & Portfolio Optimization with tidyquant (77:35)
Advanced Functional Programming
Lab 12: How I Built This - R Package Anomalize using Tidy Eval & Rlang (74:50)
Machine Learning - Beginning of Coded Labs
Lab 5: Hands-On Coding with the NEW parsnip package (75:54)
Lab 4: H2O AutoML - Erin LeDell Guest Appearance! (87:15)
Free / No-Code Labs (Before we transitioned to FULL CODE Labs)
[IMPORTANT] Labs 1-6 were made before LL PRO existed.
Lab 6: Communicating Machine Learning with the rmarkdown package (71:38)
Lab 2: R In Production: Building Production-Quality Apps with Shiny (55:32)
Lab 1: How to Learn R Fast! (56:35)
Python Instructions: VSCode, Anaconda, Environments, & General Setup / Troubleshooting Guidance
🗺️ Python Setup (VSCode & Anaconda)
🆘 Mac: Python Troubleshooting Environment Guidance
🆘 Windows: Troubleshooting Environment Guidance
Lab 12: How I Built This - R Package Anomalize using Tidy Eval & Rlang
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