Global Temperature Forecast Using Prophet and CO2

In this article I will leverage the global temperate dataset I discussed previously to make a temperature forecast using Facebook Prophet for the next 50 years. Note: the temperature dataset serves ONLY as a vehicle to learn how to do forecasting...

Google Colab and Auto-sklearn with Profiling

This article is a follow up to my previous tutorial on how to setup Google Colab and auto-sklean. Here, I will go into more detail that shows auto-sklearn performance on an artificially created dataset. The full notebook gist can be found here. First,...

Machine Learning Notes

This alphabetically sorted collection of AI, ML, and data resources was last updated on 3/26/2021. AdaBoost: Fits a sequence of weak learners on repeatadly modified data. The modifications are based on errors made by previous learners. Analysis of...

Google Colab and AutoML: Auto-sklearn Setup

Auto ML is fast becoming a popular solution to build minimal viable models for new projects. A popular library for Python is Auto-sklearn that leverages the most popular Python ML library scikit-learn. Auto-sklearn runs a smart search over...

Custom scikit-learn Pipeline

I have gone through many iterations of what my preferred scikit-learn custom pipeline looks like. As of 6/2020, here is my latest iteration. In general, a machine learning pipeline should have the following characteristics: Include every step shared...

Machine Learning Tutorial #4: Deployment

Topics: Stack Selection, Heroku, Testing In this final phase of the series, I will suggest a few options ML engineers have to deploy their code. In large organizations, this part of the project will be handled by a specialized team which is especially...

Machine Learning Tutorial #3: Evaluation

Topics: Performance Metrics, Commentary In this third phase of the series, I will explore the Evaluation part of the ML project. I will reuse some of the code and solutions from the second Training phase. However, it is important to note that the...

Machine Learning Tutorial #2: Training

Topics: Performance Metrics, Cross Validation, Model Selection, Hyperparameter Optimization, Project Reflection, Tools This second part of the ML Tutorial follows up on the first Preprocessing part. All code is available in this Github repo. Other...

Machine Learning Tutorial #1: Preprocessing

Topics: Data Cleaning, Target Variable Selection, Feature Extraction, Scaling, Dimensionality Reduction In this machine learning tutorial, I will explore 4 steps that define a typical machine learning project: Preprocessing, Learning, Evaluation, and...