Artificial intelligence applications are all around us, but what does it really mean? In this article, Kumar Abhishek explains the history and progress of artificial intelligence. … Read more
Python is a modern general-purpose programming language that's very useful for analytics. Sanil Mhatre demonstrates sentiment analysis with Python.… Read more
Many devs and IT professionals looking for the next career wonder how to become a data scientist. Ashwin Thota matches up skills to job titles.… Read more
Machine learning projects often stall when it's time to deploy. Shree Das introduces Kubeflow for data scientists, an end-to-end solution for ML projects.… Read more
Deep Learning theories have been around for many decades, but solutions have not always been practical due to hardware constraints. In this article, Shree Das explains how GPU Acceleration can help organisations take advantage of Deep Learning to speed up training of neural networks.… Read more
Like any technology, AI can be used for evil instead of good. Shree Das explains several examples and what should be done to prevent the misuse of data.… Read more
In the third article of this series, Sanil Mhatre demonstrates how to perform a sentiment analysis using R including generating a word cloud, word associations, sentiment scores, and emotion classification. … Read more
SQL Server Machine Learning allows you to run R and Python scripts from SQL Server. When SQL Server 2019 was released, Microsoft enhanced the security for this functionality, but it caused some existing code writing to the file system to to break. In this article, Dennes Torres explains the security enhancement and describes three ways to work with it. … Read more
Microsoft introduced the ML.NET framework which can be used by developers to include machine learning models in their applications. In this article, Dino Esposito discusses hosting a machine learning model in ASP.NET Core 3.0.… Read more
Machine learning is a skill that many data professionals are learning as they plan their careers over the next five to ten years. In this article, Supriya Pande gives an overview of machine learning and walks through a practical example.… Read more
Azure contains a vast array of services that can be used for machine learning, text analysis, and more. In this article, Supriya Pande provides a brief explanation of machine learning and then walks you through creating a sentiment analysis application.… Read more
The .NET F# language can be used for machine learning. In this article, Diogo Souza explains what is needed in Visual Studio to take advantage of this feature and walks you through a simple regression example. … Read more
Before you report your conclusions about your data, have you checked whether your 'actionable' figures occurred by chance? The Kruskal-Wallis test is a safe way of determining whether samples come from the same population, because it is simple and doesn't rely on a normal distribution in the population. This allows you a measure of confidence that your results are 'significant'. Phil Factor explains how to do it.… Read more
Distributed File Databases manage large amounts of unstructured or semi-structured data. They are designed on the principle of splitting up the data into multiple locations, and then placing the code that processes each fragment close, or directly on, that location. Buck Woody shows how to install Hadoop in your Data Science lab to experiment with an example of the breed.… Read more
Graph database are an intriguing alternative to the relational model. They apply graph theory to record the relationships between entries more naturally, and are a good fit for a range of data tasks that are difficult in SQL. Buck Woody gives an introduction to Graph databases and shows how to get Neo4J up and running to get familiar with the technology.… Read more
Though the Key/Value pair paradigm is common to almost every computer language, there is no clear agreement yet for the definition of a Key/Value Pair database. However, Key/Value pair databases are valuable for special applications where speed of writing data is more important than searching and general versatility. It is certainly worth experimenting with in a data science lab.… Read more
There is no better way of understanding new data processing, retrieval, analysis or visualising techniques than actually trying things out in a lab system. Buck Woody continues his series by explaining why an RDBMS is essential for a lab, what that is, and how to install SQL Server into the lab. … Read more
Although every computer language is suitable for data, some languages lend themselves especially well for working with certain types or sources of data, or processing the data in certain ways, and so are of particular use to the data scientist. … Read more
Data tools interact directly with data and are great for automating data data-aquisition, but they aren't always the best way to prototype or pilot a process. Interactive data tools also allow you to test and refine the process, until it is ripe for automation. … Read more
