All types of organizations are implementing AI projects for numerous applications in a wide range of industries. These applications include predictive analytics, pattern recognition systems, autonomous systems, conversational systems, hyper-personalization activities and goal-driven systems. Each of these projects has something in common: They're all predicated on an understanding of the business problem and that data and machine learning algorithms must be applied to the problem, resulting in a machine learning model that addresses the project's needs. Deploying and managing machine learning projects typically follow the same pattern. However, existing app development methodologies don't apply because AI projects are driven by data, not programming code.
Sometime ago, the world's most affable and recognizable AI leader, Andrew Ng launched a specialization called AI for medicine through his MOOC institution, deeplearning.ai. 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.
Image recognition is the process of identifying and detecting an object or a feature in a digital image or video. Some of its applications include systems for factory automation, face recognition, booth monitoring, and security surveillance. Image recognition is embedded in technologies that enable students with learning disabilities to receive the education they need -- in a form they can perceive. Apps powered by computer vision offer text-to-speech options, which allow students with impaired vision or dyslexia to'read' the content. By employing image recognition, Jetpac caught visual cues in the photos and analyzed them to offer live data to its users.
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.
In this course you will learn all about the mathematical optimization of linear programming for data science and business analytics. This course is very unique and have its own importance in their respective disciplines. The data science and business study heavily rely on optimization. Optimization is the study of analysis and interpreting mathematical data under the special rules and formula. The length of the course is more than 6 hours and there are total more than 4 sections in this course.
This course is bundle of two courses of linear algebra and probability and statistics. So, students will learn complete contents of probability and statistics and linear algebra. It is not like that you will not complete all the contents in this 7 hours videos course. This is a beautiful course and I have designed this course according to the need of the students. WHERE THIS COURSE IS APPLICABLE?
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 (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.
To follow along, you can either download our Jupyter notebook here, or continue reading and typing in the following code as you proceed through the walkthrough. Unsupervised machine learning methods can allow us to understand and explore data in situations where we are not given explicit labels. One type of unsupervised machine learning methods falls under the family of clustering. Getting a general idea of groups or clusters of similar data points can inform us of any underlying structural patterns in our data, such as geography, functional similarities, or communities when we otherwise would not know this information beforehand. We will be applying our dimensional reduction techniques to Microbiome data acquired from UCSD's Qiita platform.