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Learn Python for Data Science & Machine Learning from A-Z

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In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job. We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib NumPy -- A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.


5 Data Science Trends in the Next 5 Years

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This field is large enough that it's a bit impossible to deeply cover all the things that can happen in the coming 5 years for it. Important trends that I foresee but won't cover here are specific applications of Data Science in unique domains, integrating of low-code/no-code tools in the tech stack, and other narrowly-focused insights. This is going to be a focus on the general, broad themes of change I see coming to stay in the next half-decade. This isn't an exhaustive list, but it does cover a lot of the issues that are currently faced in practice today: The title of the Data Scientist has been a big issue for many in the industry mainly because of the ambiguity around what the role entails and also what the company needs. Although I believe the job descriptions have largely become clearer and concise, the job profiles are just starting to become normalized.


Recommender Systems and Deep Learning in Python

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What do I mean by "recommender systems", and why are they useful? Let's look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook. Recommender systems form the very foundation of these technologies. They are why Google is the most successful technology company today. I'm sure I'm not the only one who's accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that? Facebook: So powerful that world governments are worried that the newsfeed has too much influence on people!


5 Ways Digital Health Innovation Will Grow + Evolve Post-Pandemic

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The disruption triggered by the coronavirus (COVID-19) has induced unplanned growth across the healthcare industry. Despite these challenges, leaders in healthcare see tremendous potential in AI and analytics to deliver on the promise of higher quality care at a lower cost by empowering their executives, business leaders, clinicians, and nurses by harnessing the power of predictive and prescriptive analytics. Many healthcare organizations are seeking to harness the vast potential of Artificial Intelligence (AI) and its four components -- machine learning (ML), natural language processing (NLP), deep learning, and robotics -- to transform their clinical and business processes. They seek to apply these advanced technologies to make sense of an ever-increasing "tsunami" of structured and unstructured data, and to automate iterative operations that previously required manual processing. I have analyzed and calibrated these technologies leveraging a seminal strategy framework from John Gourville, Harvard Business School professor, predicated on the resistance to patient adoption, as well the degree of change behavior needed from physicians, clinicians, nurses, providers, payers, policy makers and the government, which will likely assure a high probability of success, in my humble opinion and will inform post-pandemic strategy blueprints and scenario/policy planning from these entities.


Python AI: Why Python is Better for Machine Learning and AI

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Today, most companies are using Python for AI and Machine Learning. With predictive analytics and pattern recognition becoming more popular than every, Python development services are a priority for high-scale enterprises and startups. Python developers are in high-demand – mostly because of what they can achieve with the language. AI programming languages need to be powerful, scalable, and readable. Python code delivers on all three.


Learning Resources for Machine Learning - Programmathically

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Familiarity with basic statistics and mathematical notation is helpful. An Introduction to Statistical Learning is one of the best introductory textbooks on classical machine learning techniques such as linear regression. It was the first machine learning book I've bought and has given me a great foundation. The explanations are held on a high level, so you don't need advanced math skills. Every chapter comes with code examples and labs in R. It is a great book to work through cover-to-cover. Get "An Introduction to Statistical Learning" on Amazon


High Performance Deep Learning, Part 1 - KDnuggets

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Machine Learning is being used in countless applications today. It is a natural fit in domains where there is no single algorithm that works perfectly, and there is a large amount of unseen data that the algorithm needs to do a good job predicting the right output. Unlike traditional algorithm problems where we expect exact optimal answers, machine learning applications can tolerate approximate answers. Deep Learning with neural networks has been the dominant methodology of training new machine learning models for the past decade. Its rise to prominence is often attributed to the ImageNet [1] competition in 2012.


Data vs. Disaster: 5 Ways Analytics Is Helping Tackle Climate Change - DATAVERSITY

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With the recent Intergovernmental Panel on Climate Change (IPPC) report painting a worrying picture of our battle against climate change, we will explore five ways analytics can help turn the tide. The UN Secretary-General, Antonio Guterres, called the report "a code red for humanity," adding that "the alarm bells are deafening and evidence irrefutable." U.S. President Joe Biden said about it, "The cost of inaction is mounting." In summary, without immediate action, the damage we've done may be irreversible. For this to change, we're going to have to rely on the latest tools and technologies, including big data, advanced analytics, modeling, and simulation techniques.


Deep Learning Works Like The Human Brain In Future - Prnotes

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Deep learning is a subfield or part of machine learning. Algorithms that replicate or inspire the human brain, consisting of algorithms designed to mimic the structure and operation of the human mind, are called artificial neural networks. It is an artificial intelligence function that mimics the human brain to process data and generates patterns used in decision-making. Unsupervised training is possible on any given data set. Deep learning is also called deep neural learning or deep neural networks.


Best 10 Artificial Intelligence Technologies That Will Change The Future

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In any event, for people to convey effectively and obviously can be interesting. Essentially, for machines to process data is an altogether unexpected process in comparison to the human brain, And it very well may be amazingly interesting and complex. Natural Language Generation is a sub discipline of AI that changes over text into information and assists the frameworks with conveying thoughts and contemplations as obviously as could really be expected. It is utilized in client care, broadly, to make reports and market rundowns. Speech Recognition is utilized to change over and change human speech into a helpful and complete organization for PC applications to process.