Learn how to increase and understand model performance with Accuracy, Precision, Recall, and F1 Score!

Learn about the different machine learning model type errors and how to quickly visualize them in a simple to understand matrix.

Learn what it means to have an overfit and underfit model and how to find the "sweet spot" between the two.

Almost all data projects involve missing or unknown values. Learn all you need to know about handling these values (NaN) in Pandas.

Learn how to structure your data science project from start to finish using the Cross-Industry Standard Process for Data Mining (CRISP-DM).

Bayes' Theorem is the foundation of Bayesian Statistics commonly used in Data Science and Machine Learning! Read more about how it works here!

Considering a MOOC to take your data science skills to the next level? Here's my experience completing the University of Michigan's Applied Data Science with Python Specialization through Coursera.

In this project, we'll use the Twitter API and Naive Bayes to predict whether a newly presented Tweet was authored by Barack Obama or Donald Trump.