130 Machine Learning Projects Solved and Explained
Great collection of hands-on tutorials that walks through a wide variety of machine learning projects using Python.
To apply AI for good, think form extraction
When people consider doing AI/ML-for-Good projects, they often think about use-cases for predictive models. But on a practical level, smart methods for extracting data from forms would do more for journalism, climate science, medicine, democracy etc. than almost any other application. Here's a great overview of the issues and opportunities.
How to Set Up a Python Project For Automation and Collaboration
Great guide for setting up a new Python project with an automated workflow of units tests, coverage reports, lint checks, and type checks that’ll catch the the majority of errors and facilitate collaboration.
Data project checklist
This collection of considerations for new data projects is based on Jeremy Howard's decades of experience with projects across a wide variety of industries. This is a post to come back to again and again.
Four questions to help accurately scope analytics engineering projects
Tristan Handy’s latest post describes how Fishtown Analytics manages to accurately scope analytics engineering projects more than 95% of the time!
How to Decide Which Data Science Projects to Pursue
In 2018, every organization has a data strategy. But what's the difference between a mediocre data strategy and one that's great? In this post, Hilary Mason, GM for Machine Learning at Cloudera and Founder at Fast Forward Labs, shares some useful insights.
How to deliver on Machine Learning projects
The process of developing machine learning models is very different than what most engineers are accustomed to. In this post, Emmanuel Ameisen describes the differences and introduces an approach he calls the "ML Engineering Loop." It's an iterative approach that enables rapid discovery and development of the best models.
A Complete Machine Learning Project Walk-Through in Python
Part 1 - Putting the machine learning pieces together
Part 2 - Model Selection, Hyperparameter Tuning, and Evaluation
Part 3 - Interpreting a machine learning model and presenting results
How to Start Your First Data Science Project
This is a great tutorial that shows how to start and finish a simple data science project using Python and Yellowbrick - from exploratory data analysis, to feature selection and feature engineering, to model building and evaluation.
Best Practices for Using Google Sheets in Your Data Project
Google Sheets can be super useful, especially if you strategize a bit beforehand. This article describes four key considerations and why, ultimately, 80% of sheet design should be for editors and only 20% for data scientists.