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Applied Text Mining and Sentiment Analysis with Python

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In this first section, we will go through several general elements setting up the starting problem and the different challenges to overcome with text data. This is also the section in which we will discover our Twitter dataset, using libraries such as Pandas or Matplotlib.


Pinaki Laskar on LinkedIn: #AI #MachineLearning #AIScience

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AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner It is as a Trans-AI to be built by the agency of transdisciplinary research and development (TRD). Today's AI is mathematical algorithms and statistical rules and predictive analytics, which excel humans in many specific areas, such as judging, strategic games, algorithmic trading, self-driving, diagnosing, computing, measuring, recognising objects, characters, faces, human speech, or translating languages. Such "narrow" AI have superhuman capabilities, but only in their specific areas of dominance, much outsmarting humans in doing specific tasks, jobs and works. Superintelligent #MachineLearning will combine a wide range of skills in one entity, as a single integrated system/network/platform of man-machine superintelligence. All what you need, it is a unifying world model (global ontology), with a unifying reasoning and learning framework (master algorithm), to intelligently process the world's data, from the web data to the real-world data.


top-4-tech-companies-working-on-augmented-intelligence

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In recent years, artificial intelligence has been dominating the global tech marketplace. Artificial intelligence, or intelligence amplification, is a cutting-edge type of technology that has made it a major part of tech companies' business models. Augmented intelligence combines machine learning with predictive analytics to improve human intelligence quickly and effectively. Multiple augmented intelligence companies have been created to provide smart functions through the introduction of intelligence amplification. The global augmented intelligence market is expected to reach US$121.5 million by 2030, with a 26.4% compound annual growth rate.


Global Big Data Conference

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Five years ago, one of Telenor's top boffins feared that Google, Amazon and Facebook were set to become an unstoppable force in artificial intelligence (AI). "There is a real risk that the most fundamental technology of the 21st century will be dominated by a few large companies, unless we take the necessary steps," said Bjørn Taale Sandberg, the Norwegian telco's head of research. For Telenor, the necessary steps meant investing in its own AI lab and backing the right startups. Conversely, it is hard to see how a strategic partnership with one of the AI bogeymen would produce alternatives to them. But that is what Telenor did five years after Sandberg first warned of an AI oligopoly, announcing a Google Cloud tie-up today. Among other things, it will be "exploring how to leverage" Google's AI expertise.


Evolution of data centers - AI-based Business Intelligence

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As we experience the dawn of the new computing era, came along with this era are the data centers. Since then, it has been facilitating data backup and recovery, managing enterprises' IT operations via a centralized location. But, as the world progresses with the evolution of new-age technologies like Artificial Intelligence and Hybrid Cloud, data centers are being considered as the core of the digital ecosystem. Enterprises are embracing data-driven business intelligence models for utilizing data to its maximum potential. As a result, data has emerged as a valuable asset and a significant part of almost every business function.


Complete Machine Learning & Data Science Bootcamp 2022

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This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. You will go from zero to mastery!


Pinaki Laskar on LinkedIn: #DataScientists #MachineLearning #DataScience

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AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner When you need #DataScientists and ML Engineers? Although they certainly work together amicably and enjoy some overlap concerning expertise and experience, the two roles serve quite different purposes. Data Scientists follow the #DataScience Process, Stage 1: Understanding the Business Problem Stage 2: Data Collection Stage 3: Data Cleaning & Exploration Stage 4: Model Building Stage 5: Communicate and Visualize Insights The majority of the work performed by Data Scientists is in the research environment. In this environment, Data Scientists perform tasks to better understand the data so they can build models that will best capture the data's inherent patterns. Once they've built a model, the next step is to evaluate whether it meets the project's desired outcome.


50-Days 50-Projects: Data Science, Machine Learning Bootcamp

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Make robust Machine Learning models Understand the full product workflow for the machine learning lifecycle. Data science can be defined as a blend of mathematics, business acumen, tools, algorithms, and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions. In data science, one deals with both structured and unstructured data. The algorithms also involve predictive analytics. Thus, data science is all about the present and future.


H2O.ai raises $100M at a $1.6B pre-money valuation for tools to make AI usable by any kind of enterprise – TechCrunch

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Now, it has raised $100 million to fuel its growth, a round of funding that values H2O.ai at $1.7 billion post-money ($1.6 billion pre-money). This is a Series E round, and it's being led by a strategic backer, the Commonwealth Bank of Australia (CBA), which has been a customer of the startup and will be using the backing to kick off a deeper partnership between the two to build new services. Others in the round include Goldman Sachs, Pivot Investment Partners, Crane Venture Partners and Celesta Capital. Further plans for the funding include building more products for H2O.ai as a whole, and hiring more talent to continue expanding the company's H2O AI Hybrid Cloud platform. This is not the first time that a customer has led a round as a strategic backer: in 2019, Goldman Sachs led the company's Series D of $72.5 million.


Data mesh: a new paradigm for data management - SiliconANGLE

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Data mesh is a new way of thinking about how to use data to create organizational value. Leading-edge practitioners are beginning to implement data mesh in earnest. Importantly, data mesh is not a single tool or a rigid reference architecture. Rather, it's an architectural and organizational model that is designed to address the shortcomings of decades of data challenges and failures. As importantly, it's a new way to think about how to leverage data at scale across an organization and ecosystems. Data mesh in our view will become the defining paradigm for the next generation of data excellence. In this Breaking Analysis, we welcome the founder and creator of data mesh, author, thought leader, technologist Zhamak Dehghani, who will help us better understand some of core principles of data mesh and the future of decentralized data management. First, Dehghani is the director of emerging technologies at Thoughtworks North America. She is a thought leader, practitioner, software engineer and architect with a passion for decentralized technology solutions and data architectures. Since we last had her on as a guest less than a year ago, she has written two books – one on Data Mesh and another called Software Architecture: The Hard Parts, both published by O'Reilly Media Inc. We're going to set the stage by sharing some Enterprise Technology Research data on the spending profile in some of the key data sectors.