Collaborating Authors

educational setting

An honest reaction to Andrew Ng's AI for medicine specialization


Sometime ago, the world's most affable and recognizable AI leader, Andrew Ng launched a specialization called AI for medicine through his MOOC institution, I have always been a big fan of Andrew Ng, and it was he who had introduced me to the world of machine learning through his grainy Youtube videos of Stanford lectures back in 2012. I was very excited that finally, Andrew Ng has finally turned his attention to the critical shortage of AI experts in the medical field . Truth be told, AI in the medical world has not seen as much progress as other domains like personalized advertisements, recommendations, autonomous driving etc. There are lot of complex issues like data privacy, small sample sizes etc. which I would prefer to discuss in depth in another post.

Zicklin Grad Students Take Top Spot in Pitney Bowes Data Challenge - Zicklin School of Business


Nearly five dozen students from Baruch College and the Zicklin School of Business got to show off their data-crunching skills recently when they participated in the Baruch College – Pitney Bowes Data Challenge, held on May 1. The winning team of Zicklin graduate students -- Drace (Yilei) Zhan (MS Statistics, '20), Nishtha Ram (MS Quantitative Methods & Modeling, '21), Huimin Chen (MS Information Systems, '21), Kang Li (MS QMM, '20), and Rosario Campoverde (MBA, '20) -- outperformed 50 other undergraduate and graduate students across Baruch and Zicklin to take first place. The competition was the culmination of a year-long collaboration among Pitney Bowes and the Paul H. Chook Department of Information Systems and Statistics, the Graduate Career Management Center, and the Starr Career Development Center. The partnership included seminars held throughout the year on machine learning, design thinking, marketing analytics, and other topics, presented by Pitney Bowes data scientists; and a free bootcamp on Python and AWS that was led by Zicklin professors. It was funded by a $10,000 grant from the NYC/CUNY Workforce Development Initiative.

Best Books to Expand Your NLP Knowledge


The abundance of knowledge and resources can be at times overwhelming specifically when you are talking about new age technologies like Natural Language Processing or what we popularly call it as NLP. When trying to educate yourself, you should always choose resources with solid base and fresh books to impart unprecedented package of learnings. Here is the list of top books that can help you expand your NLP knowledge. One of the most widely referenced and recommended NLP books, written by Stanford University professor Dan Jurafsky and University of Colorado professor James Martin, provides a deep-dive guide on the subject of language processing. It's intended to accompany undergraduate or advanced graduate courses in Natural Language Processing or Computational Linguistics. However, it's a must-read for anyone diving into the theory and application of language processing as they grow and strengthen their analytics capabilities.

Artificial Intelligence and Machine Learning in the Education Sector


Artificial Intelligence (AI) is already ubiquitous in our day-to-day lives. From maps that find the optimal route, to Amazon, Netflix and Facebook who curate content and make recommendations tailored specifically to us. Your smartphone even understands voice commands and can perform tasks prompted by you. The technology is pervasive and is increasingly being applied in the education sector. Globally in the education sector, AI is being applied in tools that help develop learner skills, allow self-paced tailored learning, streamline assessment systems, and automate administrative activities.

How to master Machine Learning during lockdown?


Machine learning is one of the most intriguing applications of Artificial Intelligence. One can start from scratch and learn it by mastering, Python, Anaconda, Jupyter, scikit-learn, NumPy, Matplotlib, and OpenCV.

Deployment of Machine Learning Models


Online Courses Udemy - Deployment of Machine Learning Models Build Machine Learning Model APIs Created by Soledad Galli, Christopher Samiullah English [Auto] Students also bought Data Science: Natural Language Processing (NLP) in Python Recommender Systems and Deep Learning in Python Artificial Intelligence: Reinforcement Learning in Python Unsupervised Machine Learning Hidden Markov Models in Python Deep Learning: Recurrent Neural Networks in Python Preview this course GET COUPON CODE Description Learn how to put your machine learning models into production. Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate.

Imposter in Data Science: 8 Tips to Overcome Your Imposter Syndrome


Depending on who you ask, Imposter Syndrome can have several meanings. The frequent feeling of not deserving one's success and of being a failure despite a sustained record of achievements. Indeed, no matter your knowledge or expertise, Imposter Syndrome can still make you feel like a complete failure. At its roots, are several factors such as previous failures, inherited fears, social biases, culture, education, and more. Being a minority in one's domain, or working in an active field of research such as Artificial Intelligence, can also trigger and worsen Imposter Syndrome.

"Transparent AI Will Revolutionize Online Learning"


Walter Bender, the Chief Learning Architect at Sorcero and the founder of Sugar Labs and One Laptop One Child, shared with IBL News how transparent AI will revolutionize online learning following his talk at the Open edX conference last month in San Diego. The main goal, he posits, is "to leverage what makes us human to become part of the learning process." His talk, "Beyond the Black Box: How Transparent AI can Transform Learning," focused on the strides that Sorcero is making with AI and online learning. With his extensive experience in academia and accessible and open online education, he says his experiences were "a case study for transparency, for providing tools and a framework." The natural extension from this was to switch gears and talk about AI, the "tool du jour in machine learning these days."

Artificial Intelligence Masterclass


Online Courses Udemy Enter the new era of Hybrid AI Models optimized by Deep NeuroEvolution, with a complete toolkit of ML, DL & AI models Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team English, Italian [Auto-generated] Students also bought Artificial Intelligence: Reinforcement Learning in Python Machine Learning and AI: Support Vector Machines in Python Advanced AI: Deep Reinforcement Learning in Python Ensemble Machine Learning in Python: Random Forest, AdaBoost Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Preview this course GET COUPON CODE Description Today, we are bringing you the king of our AI courses...: The Artificial Intelligence MASTERCLASS Are you keen on Artificial Intelligence? Do want to learn to build the most powerful AI model developed so far and even play against it? Sounds tempting right... Then Artificial Intelligence Masterclass course is the right choice for you. This ultimate AI toolbox is all you need to nail it down with ease. You will get 10 hours step by step guide and the full roadmap which will help you build your own Hybrid AI Model from scratch.