When I first heard about machine learning (ML), I thought only big companies applied it to explore big data. On searching the internet for the meaning of ML, I discovered...
Decision Trees, Random Forests and Boosting are among the top 16 data science and machine learning tools used by data scientists. The three methods are similar, with a s...
Sometimes, you see a diagram and it gives you an ‘aha ha’ moment Here is one representing forward propagation and back propagation in a neural network I saw it on Fre...
Introduction Data Science and Machine Learning are furtive, they go un-noticed but are present in all ways possible and everywhere. They contribute significantly in all t...
Building accurate models takes a great deal of time, resources, and technical ability. The biggest challenge? You almost never know what model or feature combination will...
In my previous posts, I compared model evaluation techniques using Statistical Tools & Tests and commonly used Classification and Clustering evaluation techniques In ...
Knowing when and how to choose the right statistical hypothesis test is no mean feat. It can takes years of learning and practice before you get comfortable with it. Fort...
Summary: Python’s open-source and high-level nature, as well as its comprehensive libraries, make it the perfect fit to solve the numerous real-life ML challenges. The ...
In part 1, I compared a few model evaluation techniques that fall under the umbrella of ‘general statistical tools and tests’. Here in Part 2 I compare three...
This article was written by Louis Tiao. In this series of notebooks, we demonstrate some useful patterns and recipes for visualizing animating optimization algorithms ...