Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Greetings, I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world.

In statistics and machine learning, the bias-variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples and vice versa. When building a machine learning model with a high-dimensional dataset, it is always advisable to start with a simply model, then you may add complexity as needed. During model evaluation, it is important to perform several tests to make sure your model is not capturing random effects in your dataset. To be able to detect random effects, sound knowledge of the problem that your are trying to solve is important. In this article, we illustrate the bias-variance problem using PyLab.

The Next Tsunami AI, Blockchain, IOT, and Our Swarm Evolutionary Singularity @DinisGuarda - Founder and CEO 2. AI is going to change everything? This concept is employed in work on artificial intelligence and needs to be taken in consideration as we evolve with AI and tech. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. In this complex ecosystem what is our human singularity? What is creativity in a digitalised, blockchain, nano technology - IoT AI evolutionary swarm world?

The Department of Trade and Industry (DTI) has set the wheels in motion for the crafting of the country's artificial intelligence (AI) sector roadmap to position the Philippines as an AI powerhouse in the Asean region. DTI undersecretary Rafaelita M. Aldaba led the formal signing on Thursday, November 7, of an agreement for the AI roadmap with data scientists Dr. Christopher Monterola and Dr. Erika Fille Legara of the Asian Institute of Management (AIM). "The formulation of the AI Roadmap is very important and timely. This effort provides the impetus that will move the country forward to keep up with the rapidly changing times," Aldaba said. Aldaba noted that the Philippines ranked third in the Southeast Asian region in the Government Artificial Intelligence Readiness Index 2019.

Plum, the U.K.-based "AI assistant" to help you manage your money and save more, has raised $3 million in additional funding -- money it plans to use for further growth, including European expansion. The London company has also quietly launched its app for Android phones, adding to an existing iOS app and Facebook Messenger chatbot. Backing this round -- which is essentially a second tranche to Plum's earlier $4.5 million raise in the summer -- is EBRB, and VentureFriends, both existing investors. It brings the fintech startup's total funding to $9.3 million since being founded by early TransferWise employee Victor Trokoudes, and Alex Michael in 2016. The new investment is said to come at the end of a year of "rapid expansion for Plum" in both London and Athens, including growing the team to 31 employees.

Proactively manage and communicate expectations to leadership, especially about the time and resources your project will require. Be sure to consider costs related to technology, data, people, and process. Communicate early and often to ensure everyone understands your project's progress, challenges, and new opportunities. Be sure to consider software licensing, tooling and its integration with your existing tech stack, potential data migration issues, data feature selection, and cost estimates. Stay close to the teams that work directly with the people who will use the technology, as they will strongly influence the success of your project.

Quertle is an artificial intelligence company focused on text discovery and understanding in the biomedical and life sciences fields. Published information is the foundation for the entire healthcare industry – from basic research to drug discovery to clinical trials to healthcare delivery and everything in between including business aspects. Quertle's flagship product Qinsight enables unparalleled discovery of literature through AI-powered searching, integration, organization, and presentation including predictive visual analytics. Qinsight, which covers journal articles, patents, clinical trials, treatment protocols and much more, is in use by pharmaceutical and biotechnology companies, universities, research centers, and healthcare providers around the world. Quertle was founded by Jeffrey Saffer and Vicki Burnett – Ph.D. biomedical scientists who were frustrated with the inefficiencies in discovering critical publications and the waste caused by missing information.

Quertle is an artificial intelligence company focused on text discovery and understanding in the biomedical and life sciences fields. Published information is the foundation for the entire healthcare industry – from basic research to drug discovery to clinical trials to healthcare delivery and everything in between including business aspects. Quertle's flagship product Qinsight enables unparalleled discovery of literature through AI-powered searching, integration, organization, and presentation including predictive visual analytics. Qinsight, which covers journal articles, patents, clinical trials, treatment protocols and much more, is in use by pharmaceutical and biotechnology companies, universities, research centers, and healthcare providers around the world. Quertle was founded by Jeffrey Saffer and Vicki Burnett – Ph.D. biomedical scientists who were frustrated with the inefficiencies in discovering critical publications and the waste caused by missing information.

Nearly a decade has passed since renowned venture capitalist Marc Andreessen famously declared that "software is eating the world." The subsequent years have proven his observation was a prescient one, and the software developers driving this phenomenon have risen to the top of the proverbial food chain. Now, however, it's becoming increasingly clear that they, too, are on the menu. Will development eventually make itself obsolete? The ever-increasing technological capability has forced workers in nearly every industry and sector -- engineering, government, insurance, manufacturing, and many others -- to grapple with the prospect that they might soon be made obsolete.

Adaptive sequential decision making is one of the central challenges in machine learning and artificial intelligence. In such problems, the goal is to design an interactive policy that plans for an action to take, from a finite set of n actions, given some partial observations. It has been shown that in many applications such as active learning, robotics, sequential experimental design, and active detection, the utility function satisfies adaptive submodularity, a notion that generalizes the notion of diminishing returns to policies. In this paper, we revisit the power of adaptivity in maximizing an adaptive monotone submodular function. We propose an efficient batch policy that with O(log n log k) adaptive rounds of observations can achieve an almost tight (1-1/e-ϵ) approximation guarantee with respect to an optimal policy that carries out k actions in a fully sequential setting.