This article details some of the work required both before and after the Predictive Maintenance ML solution has been developed.

This post includes an introduction to the audio domain and how to utilize audio data in machine learning. A sound classification Deep Learning model is built and steps to improve its performance are discussed. All the code is available on GitHub.

DataExplorer is a R package to simplify Exploratory Data Analysis.

In Data Science, 80% of time spent prepare data, 20% of time spent complain about need for prepare data. Of all the resources out there, DataExplorer is one of them, with its sole mission to minimize the 80%, and make it enjoyable. Most of the time, one function call is all you need.

RStudio’s Jenny Bryan has some excellent advice for improving your workflow for R in this article.

Making R Code Faster : An Etsy Case Study

This guide aims to introduce the reader to the main elements of the R ecosystem.

University of Melbourne research fellow Marcus Volz created an R package to download and visualize Strava data, and created a chart to visualize all of his runs over six years.

Self-Paced Emerging AI Developer Bootcamp Training Materials

Self-Paced Professional AI Developer Bootcamp Training Materials

This video covers how to use AI inside a mobile app to identify toys. This is done using the Azure custom vision service to generate a model to identify different toys, then shows how you can use these models from inside your app, both remotely by calling an Azure service, or locally by running the model on your device using CoreML and Tensorflow.

A brief video summary of the paper “Better Exploration with Parameter Noise”. This work is about improving reinforcement learning.

Links to the paper and source code are in the YouTube video description.

A brief video summary of Google’s “DeepMind Control Suite” paper. This paper covers benchmarking of reinforcement learning algorithms.

Links to the paper and source code are in the YouTube video description.

In this episode of the AI show Erika explains how to create deep learning models with music as the input. She begins by describing the problem of generating music by specifically describing how she generated the appropriate features from a midi file. She then describes the deep learning model she used in order to generate music.

In this episode of the AI show Paige and Lo talk about Azure notebooks. They start off with the Titanic dataset and show how to use Azure Notebooks to create a random forest classifier from scratch. The whole machine learning process is detailed from data acquisition, to data cleaning, and finally creating a machine learning model.

This episode of the AI Show is the first in a series talking about the Data Science Virtual Machine (DSVM). DSVM is a family of Azure Virtual Machine images published by Microsoft on the Azure marketplace, specially built for Machine learning, deep learning and analytics. It contains a comprehensive set of popular tools used in data analytics, machine learning and AI development – all pre-installed, configured and tested so your data science environment is ready to go.

In this episode, Dr. Perisic - Vice President of Engineering and Chief Data Officer at LinkedIn, talks about the key attributes of a data scientist, how AI and machine learning are helping personalize member experiences, why we should all be big open source fans, and how LinkedIn is partnering with other researchers through their innovative Economic Graph program to “create economic opportunity for every member of the global workforce.”

When Tatsuo Horiuchi retired, he decided to try his hand at art. But instead of spending money on paints and brushes, Horiuchi used what he already had pre-installed on his computer—Microsoft Excel. Now, the 77-year-old artist is creating remarkably intricate digital masterpieces of the Japanese landscape, all on the free graphing software.

In this book you’ll explore Redis, Neo4J, CouchDB, MongoDB, HBase, Riak and Postgres. With each database, you’ll tackle a real-world data problem that highlights the concepts and features that make it shine. You’ll explore the five data models employed by these databases-relational, key/value, columnar, document and graph-and which kinds of problems are best suited to each.