LINK: READ ORIGINAL POST HERE So far in this series, we’ve covered: In part three, we cover getting certified which effectively sums up the prior blog posts into one! MICROSOFT LEARN – CERTIFICATIONS For the unaware, Microsoft delivers free quality training for their products, and offers tests to quantify and validate said learnings. Link: Microsoft Learn Website […]
Fast-tracking Your Power BI Journey (Part 3) Read More »
Explore Microsoft Fabric, the new unified-analytics platform, and discover the limited-time free offers and official learning resources. Dive into the benefits and learn how to optimize your Fabric experience before time runs out
Learn Microsoft Fabric Free (Limited Time!) Read More »
Part one of this series explores the need to fine-tune your ‘instruments’ for quicker relevancy and success. In this section, we approach the more tactile considerations of getting from PBI novice to PBI proficiency
Fast-tracking Your Power BI Journey (Part 2) Read More »
“Practical Time Series Forecasting” by Galit Shmueli is a renowned book that skillfully guides readers through time series forecasting using R. What about those Python enthusiasts who are keen to dive into the same material? Well, we’ve got you covered! With great pride, Analytical Ants presents our first open-source translation published on Github! We have meticulously translated
Practical Time Series Forecasting – Python Version! Read More »
Everyone has heard of the 10,000 hour rule. However (spoiler alert!), it’s not always true. Veritasium published a fascinating video on this topic not too long ago (Youtube link); the crux being (if my memory serves me right!) poignant learning > passive learning. It’s better to spend fewer intentional hours learning the appropriate/excellent nuances/mannerisms than going ‘gung-ho’
Fast-tracking Your Power BI Journey Read More »
KNN as a Feature Engine can aid in ensemble learning by quantifying anecdotal knowledge through supervised machine learning.
A good example would be targeting a minority group of customers who are known to have a desirable trait (e.g., similar features/patterns in customer behavior indicative of higher ‘value’ buyers, etc.).
Part two of this two-part series incorporates the KNN Feature Engine from part one into different ensemble models and reviews the findings.
KNN as a Feature Engine with Imbalanced Data (Part 2/2) Read More »
KNN as a Feature Engine can aid in ensemble learning by quantifying anecdotal knowledge through supervised machine learning.
Part one of this two-part series reviews model transformations, metric evaluation, and practices for implementing KNN as a Feature Engine whilst giving users a self-containing script to follow.
KNN as a Feature Engine with Imbalanced Data (Part 1) Read More »
EXECUTIVE OVERVIEW Analytical Ants provides systems (“Ants”) that systematically increase operational efficiencies and yields through dynamic data insights, data architecture, and processes. We deliver these insights through a holistic approach encompassing a large portion of the data-pipeline, mainly through warehousing, machine learning, and reporting. Example services for Warehousing and Machine Learning: supervised & unsupervised ML
The Value We Provide Read More »
