- Popular Machine Learning Interview Questions - Jan 20, 2021.
Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions.
- 20 Core Data Science Concepts for Beginners - Dec 8, 2020.
With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.
- Understanding Bias-Variance Trade-Off in 3 Minutes - Sep 11, 2020.
This article is the write-up of a Machine Learning Lighting Talk, intuitively explaining an important data science concept in 3 minutes.
- Error Analysis to your Rescue – Lessons from Andrew Ng, part 3 - Jan 29, 2018.
The last entry in a series of posts about Andrew Ng's lessons on strategies to follow when fixing errors in your algorithm
- Learning Curves for Machine Learning - Jan 17, 2018.
But how do we diagnose bias and variance in the first place? And what actions should we take once we've detected something? In this post, we'll learn how to answer both these questions using learning curves.
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- No order left behind; no shopper left idle. - Oct 25, 2017.
This post is about how we use Monte Carlo simulations to balance supply and demand in a rapidly growing, high-variance marketplace.
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- 17 More Must-Know Data Science Interview Questions and Answers - Feb 15, 2017.
17 new must-know Data Science Interview questions and answers include lessons from failure to predict 2016 US Presidential election and Super Bowl LI comeback, understanding bias and variance, why fewer predictors might be better, and how to make a model more robust to outliers.
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- 4 Reasons Your Machine Learning Model is Wrong (and How to Fix It) - Dec 21, 2016.
This post presents some common scenarios where a seemingly good machine learning model may still be wrong, along with a discussion of how how to evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall.
- Top KDnuggets tweets, Aug 03-09: Understanding the Bias-Variance Tradeoff: An Overview - Aug 10, 2016.
Understanding the Bias-Variance Tradeoff: An Overview; Cartoon: Facebook #DataScience experiments and Cats; Bayesian #Machine Learning, Explained; Deep Reinforcement Learning for Keras.
- Understanding the Bias-Variance Tradeoff: An Overview - Aug 8, 2016.
A model's ability to minimize bias and minimize variance are often thought of as 2 opposing ends of a spectrum. Being able to understand these two types of errors are critical to diagnosing model results.
- How to Compare Apples and Oranges – Part 1 - Jun 17, 2016.
We are always told that apples and oranges can’t be compared, they are completely different things. Learn as an analyst, how you deal with such difference and make sense of it on a daily basis.
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- Top KDnuggets tweets, May 4-10: Understanding the Bias-Variance Tradeoff; Python, MachineLearning, & Dueling Languages - May 11, 2016.
Understanding the Bias-Variance Tradeoff; Python, MachineLearning, & Dueling Languages; Why AI development is going to get even faster; Why Implement #MachineLearning Algorithms From Scratch?
- Commonly Misunderstood Analytics Terms - Sep 3, 2015.
Unable to follow what your analyst language during presentations? Understand what exactly the common terminologies in the data science mean.
- Machine Learning in 7 Pictures - Mar 18, 2014.
Basic machine learning concepts of Bias vs Variance Tradeoff, Avoiding overfitting, Bayesian inference and Occam razor, Feature combination, Non-linear basis functions, and more - explained via pictures.
- Top KDnuggets tweets, Mar 12-13: Machine learning explained in 10 pictures; Tutorial: Using Google BigQuery - Mar 14, 2014.
Machine learning explained in 10 pictures. The most important: Bias vs Variance; A Tutorial example: Using Google BigQuery with R; Visualizing Google Analytics Data With R; Exploratory Data Analysis on Udacity: Investigate, Visualize, and Summarize Data Using R.