Yet Another Caret Workshop


You should always set the seed before calling train. Probably not the most amazing \(R 2\) value you have ever seen, but that's alright. Note that calling the model fit displays the most crucial information in a succinct way. Let's move on to a classification algorithm. It's good practice to start with a logistic regression and take it from there.

How AI can outgrow human teachers


Originally posted on The Horizons Tracker. Machine learning is typified by algorithms that are capable of deriving patterns and'intelligence' from vast quantities of training data. As company's such as DeepMind are beginning to show us however, the real improvements come when the machines are capable of learning independently of data fed it by us. A recent paper from researchers at the University of Toronto highlights some of the progress being made. The team developed an algorithm that attempts to learn from human instructions.

How to Prevent Bias in Machine Learning – Becoming Human: Artificial Intelligence Magazine


The following article is based on work done for my graduate thesis titled: Ethics and Bias in Machine Learning: A Technical Study of What Makes Us "Good," covering the limitations of machine learning algorithms when it comes to inclusivity and fairness. As Cathy O'Neil discusses in her book, Weapons of Math Destruction, the seeming impenetrability and absolute value of machine learning may not be all that we bargained for. Though machine learning appears to indisputably increase business value and efficiency, in some cases, it can sow inequality deeper by hard-coding it into our machines. It is imperative that machine learning experts, creators, and contributors account for "doing the right thing" as much as they do "meeting the bottom line" to balance the enormous power these mechanical decision makers possess. A machine learning algorithm is typically code written by a data scientist in a programming language such as R, Python, or Javascript.

Key Algorithms and Statistical Models for Aspiring Data Scientists


As a data scientist who has been in the profession for several years now, I am often approached for career advice or guidance in course selection related to machine learning by students and career switchers on LinkedIn and Quora. Some questions revolve around educational paths and program selection, but many questions focus on what sort of algorithms or models are common in data science today. With a glut of algorithms from which to choose, it's hard to know where to start. Courses may include algorithms that aren't typically used in industry today, and courses may exclude very useful methods that aren't trending at the moment. Software-based programs may exclude important statistical concepts, and mathematically-based programs may skip over some of the key topics in algorithm design.

Everything #HR Needs to Know About Machine Learning - HR Bartender


Have you seen the GE commercial about "Molly, the Kid Who Never Stops Inventing"? Every time I see it, I'm reminded about how robots are becoming a greater part of our workplace. And that's not a bad thing, but it does mean that we need to get more comfortable with today's technology concepts. That's why I'm very excited to share today's interview with you. A few weeks ago, I wrote about Kronos' new next generation workforce management solution called Workforce Dimensions.

Top Data Science & Machine Learning GitHub Repositories in March 2018


Not only can you follow the work happening in different domains, but you can also collaborate on multiple open source projects. All tech companies, from Google to Facebook, upload their open source project codes on GitHub so the wider coding / ML community can benefit from it. But, if you are too busy, or find following GitHub difficult, we bring you a summary of top repositories month on month. You can keep yourself updated with the latest breakthroughs and even replicate the code on your own machine! This month's list includes some awesome libraries.

Artificial Intelligence vs Human Intelligence


Whatever the encouraging results and the progress of #ArtificialIntelligence (#AI) the world can see, we are far from the development of an intelligence such as human intelligence. More and more studies show the major importance of our sensory relation to our environment. In his book, Descartes' Error, neuroscientist Antonio Damasio writes that "Nature appears to have built the apparatus of rationality not just on top of the apparatus of biological regulation, but also from it and with it ". In other words, the human thinks with all his body, not just with his brain. This need of physical survival in an uncertain world can be at the root of the suppleness and power of human intelligence.

Generative Adversarial Networks -- A Deep Learning Architecture


Generative Adversarial Networks (GANs)Generative Adversarial Nets, or GAN, in short, are neural nets which were first introduced by Ian Goodfellow in 2014. The algorithm has been hailed as an important milestone in Deep learning by many AI pioneers. Yann Le Cunn (father of convolutional neural networks) told that GANs is the coolest thing that has happened in deep learning within the last 20 years. Many versions of GAN have since come up like DCGAN, Sequence-GAN, LSTM-GAN, etc. GANs are neural networks composed up of two networks competing with each other. The two networks namely generator -- to generate data set and discriminator -- to validate the data set.

An Introduction to Time Series Forecasting with Prophet Package in Exploratory


We are adding Time Series Forecasting with the revolutionary amazing'Prophet' package with Exploratory v3.3! If not, then you can forecast the future and will be rich but so will everybody else. A part of the reason why it's hard is that the things like stock prices can be dramatically changed by many different conditions and irrational human behaviors influence the outcome a lot while such forecasting algorithms tend to build models based on the past data and with some reasonable logics behind. But it could be relatively easier for some other areas. For example, the population, GDP, or alcohol consumption of the United States can be forecasted relatively easier because these values won't dramatically change, so we can possibly forecast the long-term trend and that's what most of the economists at governments and institutions have been showing reasonable results.

Artificial Intelligence Presents a Golden Opportunity


Imagine if technology powered by artificial intelligence (AI) could help visually impaired people see? Such technology actually exists in the form of a smartphone app called Seeing AI that literally serves as a talking camera that helps visually impaired people see by describing their surroundings at any given moment and can improve the quality of life for millions of people. Massive amounts of data are required to fuel AI and to train the algorithms that are part of AI solutions. As the data privacy laws across the globe continue to evolve (e.g., the European Union General Data Protection Regulation that becomes effective on May 25, 2018), we continue to see significant data loss/access issues involving well-known institutions and the cybercriminals become even more sophisticated, it is of paramount importance that AI systems need to respect privacy and be highly secure. AI solutions ought to widely benefit everyone – not just a select few.