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Decisions, NLP Logix Partner to Deliver Machine Learning Capabilities to Business Process Management

decisions, nlp logix partner to deliver machine learning capabilities to business process management

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JACKSONVILLE, Fla., July 1, 2020 — The Decisions no-code workflow and rules platform was designed to enable businesses to automate and optimize their digital processes but do so in a way that is able to be done by non-programming staff. NLP Logix was founded with the mission to bring the power of machine learning to industry by becoming its customers outsourced data science team. With the combination of the Decisions platform and NLP Logix machine learning tools and team, the ability to quickly and affordably integrate artificial intelligence to workflows is now here.
“We were brought in to automate a number of financial processes for a very large non-profit,” said Matt Berseth, Lead Data Scientist for NLP Logix. “They had already deployed the Decisions platform to automate their workflows and we were able to easily embed a number of machine learning models, one of which reviewed and approved financial applications, and the efficiency gains have been amazing.”
A great example of the power of the new Partnership between Decisions and NLP Logix, is the loan origination process, which is almost entirely driven by rules and workflow and any human interactions are repetitive decisions based on experience. The Decisions platform automates the gathering …

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How Machine Learning Improves The Efficiency Of Freight Operations

how machine learning improves the efficiency of freight operations

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Just a few years ago, the state of the art in freight brokerage technology was software-assisted pricing and matching. A simple tool might take a base rate for a certain origin-destination pair and add a seasonality modifier based on historical data, then give a human broker a ranked list of the carriers most likely to service the freight. 

Then the phone calls would start, and often the broker would find out that the market had turned and that carriers weren’t available or weren’t willing to move the freight at the price originally quoted to the customer. If the broker was unwilling to take a loss on the load, it was given back to the shipper or came back with a higher price.

Today, digital freight brokerages are significantly more advanced. Pricing is based on near-time volume, capacity indicators, and carrier quality rather than just historical data, and matching is normally completed without any human intervention at all. But automation now goes far beyond those tasks. For example, Convoy uses machine learning to assess its carriers’ service quality, reduce crash risk, and combine multiple loads into round-trips, seeking out optimal routes from countless possibilities.

FreightWaves spoke with Ziad Ismail, Chief Product …

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2 books to deepen your command of python machine learning

2 books to deepen your command of python machine learning

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Image credit: DepositphotosThis post is part of “AI education”, a series of posts that review and explore educational content on data science and machine learning. (In partnership with Paperspace)
Mastering machine learning is not easy, even if you’re a crack programmer. I’ve seen many people come from a solid background of writing software in different domains (gaming, web, multimedia, etc.) thinking that adding machine learning to their roster of skills is another walk in the park. It’s not. And every single one of them has been dismayed.
I see two reasons for why the challenges of machine learning are misunderstood. First, as the name suggests, machine learning is software that learns by itself as opposed to being instructed on every single rule by a developer. This is an oversimplification that many media outlets with little or no knowledge of the actual challenges of writing machine learning algorithms often use when speaking of the ML trade.
The second reason, in my opinion, are the many books and courses that promise to teach you the ins and outs of machine learning in a few hundred pages. Now, I don’t what to vilify any of those books and courses. …

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How to master Machine Learning during lockdown?

how to master machine learning during lockdown?

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Machine Learning is an application of artificial intelligence where a computer/machine learns from past experiences (input data) and makes future predictions. This allows the machine learning models to make assumptions, test them and learn autonomously, without being explicitly programmed. It is accomplished by feeding the model with data and information in the form of observations and real-world interactions. There is a vast number of industries and applications that utilize machine learning to make themselves more efficient and intelligent.
Machine Learning came into existence in 1946 when Polish scientist Stanislaw Ulam, got frustrated while trying to figure out the probability of winning a game of solitaire. Then it was in 1959 that computer scientist, Arthur Samuel coined the term “Machine Learning”. He described it as a “Field of study that gives computers the capability to learn without being explicitly programmed”.
According to Payscale, the average salary for a Machine Learning Engineer is $111,297. As the companies race for digital disruption, the demand for machine learning as a skill has also grown exponentially in the past couple of years. So it is time to add another skill in the resume as machine learning is set to transform our future. Or maybe you can be …

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Elementary Robotics raises $13 million for its machine learning and computer vision offerings to industry

elementary robotics raises $13 million for its machine learning and computer vision offerings to industry

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Elementary Robotics raises $13 million for its machine learning and computer vision offerings to industry









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AI, Machine Learning, and the future of the gas industry

ai, machine learning, and the future of the gas industry

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There are those who think Artificial Intelligence (AI) and its subset of technologies called Machine Learning (ML), are simply new ways to do the old things. There are others who think AI, especially Machine Learning, are much more than that and represent a new way to solve new problems. I …

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How Machine Learning Impact Product Personalization

how machine learning impact product personalization

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July 1, 2020
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Machine learning-based personalization has gained traction over the years due to volume in the amount of data across sources and the velocity at which consumers and organizations generate new data. Traditional ways of personalization focused on deriving business rules using techniques like segmentation, which often did not address a customer uniquely. Recent progress in specialized hardware (read GPUs and cloud computing) and a burgeoning ML and DL toolkits enable us to develop 1:1 customer personalization which scales.
Recommender systems are beneficial to both service providers and users. They reduce transaction costs of finding and selecting items in an online shopping environment and improves customer experience. Recommendation systems have also proved to improve the decision making process and quality. In an e-commerce setting, for example, recommender systems enhance revenues, for the fact that they are effective means of selling more products. In scientific libraries, recommender systems support users by allowing them to move beyond catalog searches. Therefore, the need to use efficient and accurate recommendation techniques within a system that will provide relevant and dependable recommendations for users cannot be over-emphasized.
At Epsilon we have used machine learning to solve problems of granular product recommendations in a wide range of …

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CultivatePeople is launching an equity-minded, machine learning-based compensation product

cultivatepeople is launching an equity-minded, machine learning-based compensation product

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CultivatePeople is releasing a new product called Kamsa, a compensation software that uses machine learning to establish equal pay for employees.The three-year old company was founded by its CEO, Lola Han, as a compensation consulting and software company providing HR services to help companies retain their employees. CultivatePeople mainly targets startups and emerging tech companies looking to improve their compensation strategies and processes to help pay their employees competitively and fairly.The word Kamsa means “appreciation” in Korean, Han told Technical.ly. The new product uses machine learning to provide real-time global market compensation data to companies that need to manage employee pay, close pay gaps and establish equal pay for equal work. Kamsa matches employees to the market, so employers can provide them with a comparison analysis to other professionals in similar roles.“From planning and budgeting to processing pay increases, Kamsa guides clients through the complete compensation review process,” Han said. “This significantly reduces manual work and errors that commonly arise with using spreadsheets. Kamsa’s guided process, alongside our expert compensation consultants, provides clients with a unique and intuitive experience.”Companies can also use Kamsa to be more transparent with employees by providing career paths and …

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Primary MS in Machine Learning – Applied Study – Machine Learning – CMU – Carnegie Mellon University

primary ms in machine learning – applied study – machine learning – cmu – carnegie mellon university

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Machine Learning (ML) is a fascinating field of Artificial Intelligence (AI) research and practice where  we investigate how  computer agents can improve their perception, cognition, and action  with experience. Machine Learning is about machines improving from  data, knowledge, experience, and interaction. Machine Learning  utilizes a variety of techniques to intelligently handle large and complex amounts of  information build upon foundations  in many disciplines, including  statistics, knowledge representation, planning and control, databases, causal inference, computer systems, machine vision, and natural  language processing. AI agents with their core at Machine Learning aim at interacting with humans in a  variety of ways, including providing estimates on phenomena, making  recommendations for decisions, and being instructed and corrected. In our Machine Learning Department, we study and research the  theoretical foundations of the field of Machine Learning, as well as  on the contributions to the general intelligence of the field of  Artificial Intelligence. In addition to their theoretical education, all of our students, advised by faculty, get hands-on experience with  complex real datasets. Machine Learning can impact many applications relying on all sorts of  data, basically any data that is recorded in computers, such as health  data, scientific data, financial data, location data, weather data, energy …

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