Issue-number: 194

summary: Learning math. Tensor Studio. Case study: Agile ML. A/B Testing Guide. Dashboard conspiracy. Trolls. How black should black-boxes be? number: 194 title: Issue 194 url: published_at07.08.18, 17:47

Issue-number: 185

summary: Data Detectives. Slippery slope. JavaScript data wrangling. Fast stats. Foundations of NLP. Beautiful interactives. number: 185 title: Issue 185 url: published_at05.06.18, 19:02

Issue-number: 186

summary: ⚽ World Cup predictions. Real products versus Machine Learning. Tiny apps. Automated feature engineering. Deconstructing D3. number: 186 title: Issue 186 url: published_at12.06.18, 18:05

Issue-number: 187

summary: 🚀 Fast NLP. Model tuning & bias-variance trade-off. Forecasting w/ R. ML datasets. Comparing techniques, compared. Extract data from pdfs. number: 187 title: Issue 187 url: published_at19.06.18, 18:16

Issue-number: 188

summary: The Next Big Thing. Data engineering frameworks. Trust-based analytics. Data science vs. stats. Tracking NLP. PostgreSQL tricks & tips. number: 188 title: Issue 188 url: published_at26.06.18, 16:13

Issue-number: 189

summary: Failing at Machine Learning. Agile analytics. 2018 Big Data & AI Landscape. Interview red flags. How likely is “likely?” number: 189 title: Issue 189 url: published_at03.07.18, 18:28

Issue-number: 190

summary: Fast Pandas. Databases 101. Machine Learning w/ R. Creating a data science portfolio. MS Research Open Data. Awesome visualization research. number: 190 title: Issue 190 url: published_at10.07.18, 15:44

Issue-number: 191

summary: Totally random. What ML practioneers *really* do. Data oaths. ML conferences around the world. How-to guide for reading papers. number: 191 title: Issue 191 url: published_at17.07.18, 16:28

Issue-number: 192

summary: Think you’re data-driven? How to value data. Decision trees tutorial. Evolutionary algorithms. Model services for resource constrained orgs. number: 192 title: Issue 192 url: published_at24.07.18, 16:48

Issue-number: 193

summary: 💰 for data. Less bias? Code to cure. The R generation. The modern data science org. How to determine open source project health. Uncertainty viz. number: 193 title: Issue 193 url: published_at31.07.18, 16:27