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Deep Learning

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Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. A course to master this important area of Artificial Intelligence. Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones. Specifically, it is a type of machine learning, a technique that allows computer systems to improve with experience and data. Deep Learning is about how the Artificial Intelligence systems can utilize the multiple layer models of human brain and do the things which only humans can do efficiently at present.


Different ways to Implement Machine Learning with Oracle Analytics

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Predictive Analytics is one of the widely used flavours of Analytics. Nowadays, most of the customers want to leverage machine learning(ML) techniques to identify the likelihood of future outcomes based on historical data. To predict the future KPIs appropriate Machine learning Models require to be developed and used for predictive analytics. This blog is primarily focusing on how to implement machine learning with Oracle analytics to predict future KPIs and then perform analytics in Oracle Analytics Cloud(OAC) or Oracle Analytics Server(OAS). "Please do not use this blog to refer and validate Machine Learning concepts" We can implement ML either in Oracle Analytics Cloud/Oracle Analytics Server or in Oracle Database.


Version Next Now Season 4 -- Greg Kihlström Customer Experience & Digital Transformation

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For years, organizations have leveraged business intelligence dashboards to help users make data-driven decisions. Unfortunately, often the analytics platforms are chosen to fit the data rather than leading with what the company is trying to solve for. The sheer volume of data and lack of context provided can lead to poor decisions and less than ideal outcomes. That's where decision intelligence comes in. Leaders from TEKsystems share their points of view on how organizations are incorporating AI and machine-learning technologies to transform their business intelligence platforms into powerful tools that optimize the decision-making process, create agility and drive the business forward.


3 Strategies To Redefine Your Executive Career Path With AI

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Artificial Intelligence (AI) is disrupting businesses and job roles in every industry, causing concerns about long-term job security for low-skill manual jobs and management roles alike. To prepare for this AI-driven economy, many experienced managers and seasoned executives are turning to MOOCs (Massive Open Online Courses) to upskill in foundational data analytics and AI. This trend is unlikely to slow down anytime soon: The global MOOC market is expected to grow from $3.9 billion in 2018 to $20.8 billion by 2023, a CAGR of 40.1 percent. Business and technology-related courses make up 40 percent of these online courses. Many universities have also joined the drive to fill the AI leadership gap by offering high-touch executive education programs.


Building a culture of pioneering responsibly

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As chief operating officer of one of the world's leading artificial intelligence labs, I spend a lot of time thinking about how our technologies impact people's lives – and how we can ensure that our efforts have a positive outcome. This is the focus of my work, and the critical message I bring when I meet world leaders and key figures in our industry. For instance, it was at the forefront of the panel discussion on'Equity Through Technology' that I hosted this week at the World Economic Forum in Davos, Switzerland. Inspired by the important conversations taking place at Davos on building a greener, fairer, better world, I wanted to share a few reflections on my own journey as a technology leader, along with some insight into how we at DeepMind are approaching the challenge of building technology that truly benefits the global community. In 2000, I took a sabbatical from my job at Intel to visit the orphanage in Lebanon where my father was raised. For two months, I worked to install 20 PCs in the orphanage's first computer lab, and to train the students and teachers to use them.


Improving Decision Making with Intelligent Applications

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So, how do organizations empower their employees with machine learning? One option is to completely disregard the end-user experience and make the model available behind some endpoint where the user can directly query the model. This is, of course, technically complex and not entirely intuitive. For example, a user within their workflow identifies a gap in their knowledge that can be addressed with an available machine learning model, determines what information will adequately allow the model to fill that knowledge gap, and then selects the correct input to pass into the model. Next, the user exits their workflow to e.g. a command-line interface, Jupyter notebook, etc. to make a call to the model deployed model, receives a JSON output from the model endpoint, parses the model output to determine the answer to their original question, and then returns to their original workflow armed with the model output.


Machine Learning for Accounting with Python

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This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems. Accounting Data Analytics with Python is a prerequisite for this course. This course is running on the same platform (Jupyter Notebook) as that of the prerequisite course.


Machine Learning and Reinforcement Learning in Finance

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This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.


Developing AI Applications on Azure

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This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning. Next, this course introduces the machine learning tools available in Microsoft Azure. We'll review standardized approaches to data analytics and you'll receive specific guidance on Microsoft's Team Data Science Approach.


K-12 Artificial Intelligence Market Set to Explode by 2024

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The use of artificial intelligence in education is expected to explode to a worldwide market value of $6 billion over the next six years, with about 20 percent of that growth coming from applications for U.S. K-12 classrooms and consumers, according to a report by Global Market Insights. In fact, the U.S. education market combined--consisting of K-12, higher education and corporate training--represents more than half of that anticipated growth, reaching about $3.4 billion by 2024. Of that, about $1.2 billion is expected to come from K-12 uses, the Selbyville, Del.-based market research firm indicated. That's a far cry from where the nascent industry started. In 2017, artificial intelligence--broadly defined as the attempt to simulate intelligent behavior in computers that is similar to the functions of human behavior--accounted for more than $400 million among all education segments worldwide, including higher education and corporate training purposes, according to the study.