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5 Free Resources for Getting Started with Self-driving Vehicles
This is a short list of 5 resources to help newcomers find their bearings when learning about self-driving vehicles, all of which are free. This should be sufficient to learn the basics, and to learn where to look next for further instruction.
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 What Advice Would You Give Your Younger Data Scientist Self?
I was asked this question recently via LinkedIn message: "What advice would you give your younger data scientist self?" The best piece of advice I honestly think I can give is this: Forget about "data science."
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 The Practical Importance of Feature Selection
Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing generalizability.
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Machine Learning Workflows in Python from Scratch Part 2: k-means Clustering
The second post in this series of tutorials for implementing machine learning workflows in Python from scratch covers implementing the k-means clustering algorithm.
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Is Regression Analysis Really Machine Learning?
What separates "traditional" applied statistics from machine learning? Is statistics the foundation on top of which machine learning is built? Is machine learning a superset of "traditional" statistics? Do these 2 concepts have a third unifying concept in common? So, in that vein... is regression analysis actually a form of machine learning?
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7 Steps to Mastering Data Preparation with Python
Follow these 7 steps for mastering data preparation, covering the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.
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Machine Learning Workflows in Python from Scratch Part 1: Data Preparation
This post is the first in a series of tutorials for implementing machine learning workflows in Python from scratch, covering the coding of algorithms and related tools from the ground up. The end result will be a handcrafted ML toolkit. This post starts things off with data preparation.
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Must-Know: Key issues and problems with A/B testing
A look at 2 topics in A/B testing: Ensuring that bucket assignment is truly random, and conducting an A/B test on an opt-in feature.
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 The Guerrilla Guide to Machine Learning with R
This post is a lean look at learning machine learning with R. It is a complete, if very short, course for the quick study hacker with no time (or patience) to spare.
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5 Machine Learning Projects You Can No Longer Overlook, May
In this month's installment of Machine Learning Projects You Can No Longer Overlook, we find some data preparation and exploration tools, a (the?) reinforcement learning "framework," a new automated machine learning library, and yet another distributed deep learning library.
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What Advice Would You Give Your Younger Data Scientist Self?
The Practical Importance of Feature Selection
Is Regression Analysis Really Machine Learning?
Machine Learning Workflows in Python from Scratch Part 1: Data Preparation
The Guerrilla Guide to Machine Learning with R