ML crash course. Systems approach to data. Practical linear algebra. AI for business - what it can and can't do. Citizen science.
Message Board. Startup success w/ AI. Strategies for unstructured text. Technical debt in ML. When not to use deep learning. The Data PM.
Summer reading list. C-suite analytics. Cryptocurrency analysis. TensorFlow tutorials. PyData 101. Visualization how-to.
Curated Jupyter notebooks. Data engineering ecosystem. Real-world modeling. Web scraping tutorial. Data viz pitfalls.
What are you reading? Hacking history. Infrastructure guidelines: small/medium/big data. Deep learning foundations. Datashader.
Paradoxes of probability. AI startup opportunities. R in production. Time series anomalies. Network viz.
Hands-on ML. Big BS. Algorithmic trading. You're not ready for AI. Crowdsourcing analysis.
Distributed tyrants. AI at work. Productivity for data science. Build your own DL box. Problems w/ maps.
Not-Hotdog classifier. Production machine learning. Garry Kasparov on AI. Data viz w/ Python. Deep learning in the real world.
Promise of AI. Measuring repetition. Cracking privacy. SQL for your file system. OpenVis presentations.