Determining whether a mushroom is edible with machine learning

It's been awhile since my last blog post but we've been busy with a big move from Houston to Brooklyn. The opportunities in New York City for data science and AI seem endless! I've also been spending some time putting to practice my newly acquired knowledge of machine learning by browsing through open datasets.

One dataset that piqued my interest is the mushroom dataset from the UCI Machine Learning Repository describing different species from the genera Agaricus and Lepiota. The data are taken from The Audubon Society Field Guide to North American Mushrooms, which states "there is no simple rule for determining the edibility of a mushroom". Challenged by this bold claim, I wanted to explore if a machine could succeed here. In addition to answering this question, this post explores some common issues in machine learning and how to use Python's go-to machine learning library, Scikit-learn, to address them.

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Applying k-means clustering to flow cytometry analysis

Is it possible for a machine to group together similar data on its own? Absolutely—this is what clustering algorithms are all about. These algorithms fall under a branch of machine learning called unsupervised learning. In this branch, we give a machine an unlabeled training set containing data regarding the features but not the classes. Algorithms are left to their own devices to discover the underlying structure concealed within the data. This is in stark contrast to supervised learning, where the correct answers are available and utilized to train a predictive model.

In this post, I'd like to introduce an algorithm called $k$-means clustering and also construct one from scratch. Additionally, I'll demonstrate how this algorithm can be used automate an aspect of a widely used life sciences technique called flow cytometry.

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Iterables, iterators and generators, oh my! Part 2

In a previous post, we learned about iterators—one of the most powerful programming constructs. Our discussion divulged their role as a fundamental but hidden component of Python's for loop, which led to a startling revelation regarding the for loop itself (no spoilers here). We also discovered how to implement the iterator protocol to create our very own iterators, even constructing ones that represent infinite data structures. In this post, I'd like to build upon our knowledge and introduce a more elegant and efficient means for producing iterators. However, if you're not comfortable with the iterator protocol and the inner workings of iterators, I strongly recommend familiarizing yourself with Part 1 first.

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Building a logistic regression classifier from the ground up

The logistic regression classifier is a widely used machine learning model that predicts the group or category that an observation belongs to. When implementing this model, most people rely on some package or API: just hand over a dataset and out come the predictions. However, I'm not a fan of using black boxes without first understanding what's going on inside. In fact, lifting the hood on this classifier provides a segue to more complex models such as neural networks. Therefore, in this post, I'd like to explore the methodology behind logistic regression classifiers and walk through how to construct one from scratch.

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Iterables, iterators and generators, oh my! Part 1

Iterators and generators are among my favorite programming tools—they're also some of the most powerful. These constructs enable us to write cleaner, more flexible and higher performance code; undoubtedly an invaluable addition to any programmer's toolbox. In addition, iterators and generators are an elegant means to work with large and potentially infinite data structures, coming in handy for data science. However, they can be some of the more perplexing concepts to grasp at first.

In this article, I'd like to deliver a gentle but in-depth introduction to iterators and generators in Python, although they're prevalent in other languages too. Nevertheless, in order to appreciate generators, we need to first have a good handle on iterators. And to understand iterators, we need to start with iterables.

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Exploring the Pokemon dataset with pandas and seaborn

The Pokemon dataset is a listing of all Pokemon species as of mid-2016, containing data about their type and statistics. Considering how diverse Pokemon are, I was interested in analyzing this datset to learn how the game is balanced and to potentially identify the best Pokemon, if there exists one. Plus, it's a good excuse for me to practice exploratory data analysis with Python's open-source libraries: Pandas for data analysis and Seaborn for visualizations.

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What exactly is data science?

I figured I'd focus my first post on a broad topic and what better way than to discuss what this blog will revolve around: data science! Actually, when I talk to most people about data science, I usually get blank stares. This is understandable because data science is an emerging field—practically everyone has their own definition, so I'd like to begin by sharing mine.

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