Battle of the Auto ML titans for People Analytics application


How powerful is it to look into the future and predict something that is yet to happen? In Data Science, Machine Learning enables this! Ingeniously, by learning from what has happened in the past to predict what might potentially happen in the future. As we can imagine, the applications of such a technique can be revolutionary in People Analytics, one classic use case being to predict employee resignations.

Read More

7 steps to design a basic Neural Network (part 2 of 2)

In Part 1 of 2 of this segment, we saw the limitation of using a traditional prediction model like logistic regression to correctly classify two colors in a noisy dataset. Then, we built our own neural network structure, initialized parameters, and computed the forward propagation activation functions. In this Part 2 of 2, we will complete the build of our neural network model to better classify the color dots in our original dataset. Specifically, we’ll review the cost function, backward propagation, parameters updates, and the final model assembly/prediction.

Read More

7 steps to design a basic Neural Network (part 1 of 2)

This two-part article takes a more holistic, overarching (and yes, less math-y) approach to building a neural network from scratch. Python for completing the network is also included in each of the 7 steps. Part One: (1) Define the network structure, (2) Initialize parameters, and (3) Implement forward propagation. Part Two: (4) Estimate cost, (5) Implement backward propagation, (6) Update parameters, and (7) Make predictions.

Read More

2020 NFL postseason predictions from Machine Learning Model — Conference

Bills favored due to surging offense and steady defense, Packers with slim edge. Over the first two weeks of the NFL playoffs, I shared my model (V 5.0) predictions (Wild Card, Divisional). After 10 games, model performance has almost exactly matched performance in the test set for predicting winners (actual: 7/10, 70%; test set: 69.6%).

Read More

Prediction 2021: The Year AI Became Normal

A clear pattern of growth has already emerged in AI: in 2018–19, the phase of experimentation became mature; in 2020, adoptions began in a serious way and suddenly, COVID-19 gave the business leaders an opportunity and impetus to push automation and AI. In 2021, the fallout from a second wave of COVID-19 will eventually become clear, starting with the rapid decline of many traditional, non-digital businesses. As the C-suite takes notice, following are the relevant trends I expect to emerge in 2021.

Read More

New self-supervised AI models scan X-rays to predict prognosis of COVID-19 patients

x-ray image

Researchers from Facebook and NYU Langone Health have created AI models that scan X-rays to predict how a COVID-19 patient’s condition will develop. The team says that their system can forecast whether a patient may need more intensive care resources up to four days in advance. They believe hospitals could use it to anticipate demand for resources and avoid sending at-risk patients home too early.

Read More