Three simple rules to make beautiful tSNE plots Image source This is the second post of the column Mathematical Statistics and Machine Learning for Life Sciences. In the first post we discussed whether and where in Life Sciences we have Big Data suitable for Machine / Deep Learning, and emphasized … Read moreHow to tune hyperparameters of tSNE
This month, 50 years ago, the wildest science-fiction fantasies became reality when Neil Armstrong stepped off the Lunar Module ladder and left his iconic footprint on the moon’s surface. His infamous words, “one small step for man, one giant leap for mankind,” reverberated across the globe, to an audience of … Read more50 Years After the Moon Landing, What Is the Future of Space Exploration? [Infographic]
Key Concepts to Remember When Comparing Models In a previous article we discussed the concepts of underfitting and overfitting, how they can lead to models that don’t match the available data, how to identify each issue, and how to identify models that do fit the data well. These concepts can … Read moreHow to Choose Between Multiple Models
A step-by-step approach to finding, evaluating and using open source neural network models Photo by Luca Bravo on Unsplash Machine learning continues to produce state of the art (SOTA) results for an increasing variety of tasks and more companies are looking to ML to solve their problems. With the incredibly … Read moreA Recipe for using Open Source Machine Learning models
In Python, a file is characterized as either text or binary, and the difference between the two is important Text files are structured as sequence of lines. Each line is terminated with a special character called EOL or End of line character. There are several types but most common are … Read moreData Scientist’s toolkit — How to gather data from different sources
Applying Linear Regression to Medicare Data As Metis Data Science Bootcamp students, we were tasked to build a linear regression model for our first individual project. Simple enough right? Just draw a line through data. Not so fast! As a healthcare professional, I was excited to use this algorithm to … Read morePredicting How Expensive A Healthcare Provider is for the Government
An example of a negatively skewed distribution is age at death. In this dataset, there are, unfortunately, some deaths at a relatively young age, lowering the mean and skewing the graph leftward (negatively). Age at death in Australia, a negatively skewed distribution (Source) In the case of a negative skew, … Read moreHow 90% of Drivers Can be “Above Average” or Why You Need to be Careful When Talking Statistics
Chapter 9 & 10 — Learning without labels and other forms of learning Unsupervised learning is when your data doesn’t have labels. It’s a hard problem because you don’t have a ground truth to judge your model against. The book looks at two ways of dealing with unlabelled data, density … Read moreThe Hundred-Page Machine Learning Book Book Review
Because generating new content doesn’t need to be complicated The most interesting applications of Machine Learning are, without a doubt, the generative models. The idea of finding patterns in data and generating new content that at the same time is similar to your data, but unique in its own way, … Read moreGenerating Startup names with Markov Chains
Say hello to your sixth sense When I first took science class in elementary school, we learned about the five senses. Maybe not far into the future, that information might be as outdated as the idea of nine planets in our solar system (sorry, Pluto). This might be thanks to … Read moreWhat Is Neuralink: A Look At What It Is, What It Wants to Be, and What It Could Become