Machine learning can offer your business plenty of benefits that unfortunately come hand in hand with plenty of security vulnerabilities. This article explains how secure machine learning is, and provides you with a list of the main risks that you should guard against.
Data and tools are supposed to help salespeople effectively do their job, but there are too many tools and too many integrations that sales teams must deal with. To use data effectively, companies must work towards consolidating workflows, to free up the salesperson's time to build a relationship of trust with buyers.
Although every organization hopes for a successful data science project, the unfortunate reality is that plenty of projects fail. That happens because the data science process needs to have a very clear and well organized plan. In this post, we introduce a roadmap of 8 steps for a successful data science process.
Artificial intelligence can be used to improve the banking industry. Banks need to improve their current legacy systems and infrastructure and also to up-skill their employees to ensure the usage of their systems more efficiently.
The shift to working from home that we have seen since the beginning of the pandemic has its drawbacks, but the office will need to adapt to new work patterns. Content and services that are customized to meet companies’ needs are the future of the learning and development industry. The privacy of learners' data within their workplaces is an important topic that needs more discussion.
How would you feel about hiring someone with only 70% expertise in their specialty? In this second part of the ‘Future of Learning’ episode, Claudia Virlanuta talks to Allarium Co-Founder, Dan Mackey about the importance of a shift towards 100% mastery within learning and development, and how data and automation can help reach this goal.
Most companies have lots of custom training material - recorded talks, webinars and presentations - that is not being put to good use. Allarium founder Dan Mackey discusses the best ways to reuse content you already have for creating microlearning experiences.
What if fashion brands asked their customers what they want to wear before (over)producing new designs? In this episode, Claudia Virlanuta talks to Sustalytics CEO, Julie Evans about the gap between the fashion produced each year and what consumers want, and how to close it with data.
Although there are efforts dedicated to it, emotion recognition is not as mainstream as some other applications of Artificial Intelligence. In this article, we explore how AI-assisted emotional recognition can improve teaching outcomes.
Deploying AI solutions is a priority for many businesses. This article describes the eight steps needed to develop, deploy and maintain an AI project that successfully solves a business problem. Read on to learn about the complete AI project life cycle that will maximize your return on investment.
As Machine Learning (ML) gains traction, and quickly becomes a technology that every company wants to implement, those same companies start becoming aware of the challenges that come along with it. Among the challenges: creating and training a ML model is a lot simpler than actually deploying and using that ML model in a practical way.
There are many machine learning styles to choose from. In this guide, I aim to give you a brief introduction to the most important machine learning types in use today, and when to use each. Specifically, we will go over machine learning by type of training, machine learning by learning volume, and machine learning by style of learning.
What is data analytics? How about predictive analytics? How are these two different from data science? People use different terms to describe the fields of analytics and data science. Due to the exploding popularity of data science and big data analytics, many of these terms are used incorrectly and in a confusion fashion.
In this Quick Guide, we will learn markdown, a very useful and very simple markup language you can use for writing formatted plaintext.
In this quick blog post we will learn how to include comments in our code. Though comments are not executed by the computer, they are a fundamental part of writing good, maintainable code.
In this blog post we will learn a simple mental model for how computer memory works. We will then use this mental model to understand Python variables.
In this quick guide, we will cover the basics of Jupyter Notebooks. After reading this, you will be ready to make the most out of this powerful computing environment, which is the workhorse of data analysts and data scientists around the world.
Previously, we learned about data types in programming and we briefly went over the list of basic data types that we have available in Python. In this blog post, we begin diving into the Python data types to learn what they can do for us.
The terms machine learning, deep learning and artificial intelligence are often used interchangeably. But are they exactly the same thing?
Step by step instructions on how to open a Jupyter Notebook in Mac OS and Windows, with screenshots.
What is a program? What is it made of? In this article we begin exploring computer programs in more details. Particularly, we'll be looking at data types: what they are, and why they are a fundamental concept in programming.