Talk by Ekaterina Kochmar, University of Cambridge, at the Cambridge Coding Academy Data Science Bootcamp: https://cambridgecoding.com/datascience-bootcamp
Data Science and Artificial Intelligence, are the two most important technologies in the world today. While Data Science makes use of Artificial Intelligence in its operations, it does not completely represent AI. In this article, we will understand the concept of Data Science vs Artificial Intelligence. Furthermore, we will discuss how researchers around the world are shaping modern Artificial Intelligence.
What is Data Science?
Data Science is the current reigning technology that has conquered industries around the world. It has brought about a fourth industrial revolution in the world today. This a result of the contribution by the massive explosion in data and the growing need of the industries to rely on data to create better products. We have become a part of a data-driven society. Data has become a dire need for industries that need data to make careful decisions.
What is Artificial Intelligence?
Artificial Intelligence is the intelligence that is possessed by the machines. It is modeled after the natural intelligence that is possessed by animals and humans. Artificial Intelligence makes the use of algorithms to perform autonomous actions. These autonomous actions are similar to the ones performed in the past which were successful.
How is Artificial Intelligence Different from Data Science?
Let’s start exploring Data Science vs Artificial Intelligence through the below points –
1. Constraints of Contemporary AI
Artificial Intelligence and Data Science can use interchangeably. But there are certain differences between the two fields. The contemporary AI used in the world today is the ‘Artificial Narrow Intelligence’. Under this form of intelligence, computer systems do not have full autonomy and consciousness like human beings. Rather, they are only able to perform tasks that they are trained for. For example, an AlphaGo may be able to defeat the world’s No. 1 Go champion, but he will not know that it is playing the game of AlphaGo. That is, it does not have a conscious mind.
Let us know more about AI and Data Science in detail:
Artificial Intelligence In the present, is mind-boggling and viable however no place close to human knowledge. People utilize the information exhibit around them and the information gathered in the past to make sense of everything without exception. In any case, AIs don’t have that capacity right now. AIs simply immense information dumps to clear their goals. This implies AIs require a colossal pool of information to accomplish something as straightforward as altering letters. Colloquially, the expression “man-made brainpower” is connected when a machine emulates “psychological” capacities that people connect with other human personalities for example “learning” and “critical thinking”
The extent of AI is debated: as machines turn out to be progressively proficient, assignments considered as requiring “insight” are regularly expelled from the definition, a wonder known as the AI impact, prompting the jest “AI is whatever hasn’t been done yet.
For example, optical character acknowledgment is habitually avoided by “man-made brainpower”, has turned into a routine technology. Capabilities by and large delegated AI starting in 2017 incorporate effectively understanding human speech, contending with an abnormal state in vital diversion frameworks, complex information, including pictures and recordings. Various models such as Bernoulli Model, naive Bayes model, etc.
Data Science is an interdisciplinary field of procedures and frameworks to extract learning or bits of knowledge from information in different structures. This implies information science enables AIs to make sense of answers to issues by connecting comparative information for some time later.
In a general sense, information science takes into consideration AIs to discover proper and significant data from those colossal pools speedier and all the more productively.
A case of this is Facebook’s facial acknowledgment framework which, after some time, accumulates a great deal of information about existing clients and applies similar methods for facial acknowledgment with new clients. Another illustration is Google’s self-driving autos which accumulate information from its surroundings progressively and forms those data to settle on smart choices out and about.
Data Science is an “idea to bring together measurements, information investigation, and their related strategies” so as to “comprehend and dissect real wonders” with data. It utilizes systems and speculations drawn from numerous fields inside the expansive regions of arithmetic, insights, data science, and software engineering, specifically from the subdomains of machine learning, characterization, group examination, vulnerability evaluation, computational science, information mining, databases, and representation.
#Data_Science, #Artificial_Intelligence, #PandeyGuruji
April 9, 2018
Technology is growing with each day and its growing speed is something we have never seen before. We didn’t know what smartphones were two decades ago but now, they’ve become almost a part of our bodies.
A big majority of people have become daily users of social media platforms and similar sites which have been around for decades. Technology’s impact on our daily lives has been quick and powerful, but how do you think its impact on our professional lives will be?
Engineers’ professional lives always involve technology, and the latest developments in the artificial intelligence area can change the engineering concepts radically. Robotics and engineering go hand in hand, and the future of AI has the potential to make human life easier and safer than ever before.
According to Elon Musk, Tesla’s Autopilot feature can help in reducing highway accidents thanks to its intelligent engineering. Also, founders and CEOs of big technology corporations such as Bill Gates, Sundar Pichai, and Tim Cook have also stated the importance and potential of artificial intelligence for humankind.
Artificial intelligence can help us find new solutions to our engineering problems and make our tasks easier to tackle.
Technological developments have been quick and efficient for the past decades and it seems that this snowball effect has not reached its full potential yet. Artificial intelligence holds great opportunities for tomorrow’s engineers, and if you want to learn more about the subject, be sure to watch our video!
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Statement by Anya Daneez Khan, age 10, on the occasion of the International Day of Women and Girls in Science (11 February).
February 11th marks the International Day of Women and Girls in Science. The theme for 2019 is “Investment in Women and Girls for Inclusive Green Growth,” and a two-day event began today (11 Feb) at UN Headquarters in New York, bringing together global experts and leaders to evaluate the economic and social impact of women’s participation in science-based sustainable development programmes.
The event featured a high-level panel focusing on the public-sector financing of science for green growth, investment to attract and retain high calibre women in science, and financing to ensure gender equality in science.
Anya Daneez Khan, a girl in the field of science, said “The reason we celebrate this Day is to make sure it becomes not a story about exceptional women but a norm that girls belong and succeed in science and technology.”
The United Nations General Assembly in 2015 declared 11 February as the International Day of Women and Girls in Science in order to achieve full and equal access to and participation in science for women and girls, and further achieve their empowerment as well as gender equality.
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A panel of international experts on Artificial Intelligence (AI) techniques applied to Environmental Sciences, including government, academic and private sector, will discuss the role of AI on current applications, and will provide their perspectives on the challenges and potential applications of novel AI methods. The panel will discuss regression and classification-based approaches, as well as examples of supervised and unsupervised learning problems. Specific examples, best practices and lessons learned will be highlighted. Outcomes from the session will be used to provide a framework to facilitate the integration of Artificial Intelligence and Environmental Science research initiatives.
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This What is Data Science Video will give you an idea of a life of Data Scientist. This Data Science for Beginners video will also explain the steps involved in the Data Science project, roles & salary offered to a Data Scientist. Data Science is basically dealing with unstructured and structured data. Data Science is a field that comprises of everything that is related to data cleansing, preparation, and data analysis.
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0:10 Life of a Data Scientist
– Steps in Data Science project
– Understanding the business problem
– Data acquisition
– Data preparation
– Exploratory data analysis
– Data modeling
– Visualization and communication
– Deploy & maintenance
3:11 Roles offered to a Data Scientist
3:53 Salary of a Data Scientist
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This Post Graduate Program in Data Analytics, in partnership with Purdue University and in collaboration with IBM, will make you an expert in data analytics. In this Data Analytics course, you’ll learn analytics tools and techniques, the languages of R and Python (with no prior programming experience required), how to create data visualizations with Tableau, and how to apply statistics and analytics in a business environment. Simplilearn’s PGP in Data Analytics will provide you with extensive expertise in the booming data analytics and data science fields.
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8. Masterclasses from Purdue faculty
1. Data analytics
2. Statistical analysis using Excel
3. Data analysis: Python & R
4. Data visualization: Tableau & PowerBI
5. Linear and logistic regression modules
6. Clustering using k-meansSupervised learning
Fast track your career in the data analytics field via a comprehensive curriculum covering the concepts of data analytics and statistics foundation, analyzing data using Python and R programming languages, interacting with databases using SQL, and visualizing the data using Tableau and powerBI.
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Ever wondered how to take a photo from a galaxy which is millions of light-years away? Lean back and enjoy the beauty of our universe…
#TXGroup #SDSC #ETHZ #EPFL #TXConference #TXConference2020
TX | Conference is the yearly digital exchange conference focused on Marketing, Product and Technology within TX Group AG.
This year the TX | Conference has transcended physical reality into the Metaverse!
TX Group is a network of media and platforms offering information, orientation, entertainment and services to over 80 percent of the Swiss population every day.
Will AI Take Over the Jobs? | Jack Ma Speaks on AI Vs Machine Learning Vs Data Science
This video is selected and uploaded by Acadgild to educate the people on Artificial Intelligence, Machine Learning, and Data Science. This video is not our property.
The broader waves of Technology revolution like Artificial Intelligence and Machine Learning is sweeping the whole world. These are being heralded as the key to our civilization’s brightest future and tossed on technology’s over-reaching propeller heads. “Next 30 years will be Painful as the Machine Learning will take away your jobs if you are not creative and innovative enough”, says Jack Ma, Founder and Executive Chairman of Alibaba Group during an interview with CNBC pointing out data will be important to human life in the future by making machine to support the human beings instead of putting their jobs at stake.
Big Data and Hadoop Certification Training – https://acadgild.com/big-data/big-data-development-training-certification?aff_id=6003&source=youtube&account=Dd5j7ccujew&campaign=youtube_channel&utm_source=youtube&utm_medium=jackma&utm_campaign=youtube_channel
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Check out the steamy conversation between Jack Ma and CNBC’s David Faber.
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Data science combined with artificial intelligence is set to create change that this world has never seen. What can computers do and what will they be able to do in order to impact the world. Fred Blackburn tackles some of the country’s most critical issues–terrorist activities, drug interdiction, and even driverless vehicles. He leads the Justice, Homeland Security, and Transportation division of Booz Allen Hamilton, a top consulting firm based in Washington, D.C. Previously, he led both the analytics and predictive intelligence business areas for commercial, civil, defense, and security clients. He is an expert as using data and analytics to solve today’s complex problems. Before joining Booz Allen in 2008, Fred headed up the management consulting division at Northrop Grumman/TASC. As a director, he was responsible for acquisition management, decision analysis, cost and risk analysis, financial management, intelligence analysis, and change management. Recognized for his achievements, Fred was awarded two Director of Central Intelligence Meritorious Unit Citations for his contributions to the Future Imagery Architecture Joint Management Office and his support to the National Geospatial Intelligence Agency. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
#Freelancing #FreelanceDataScience #Upwork
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This is a special episode of the Bionic Bug Podcast streamed LIVE on YouTube on November 12 at 7:00PM EST. Join me for a riveting interview with Mr. Samuel Bennett to talk AI and Robotics. Mr. Bennett is currently a researcher at the Center for Naval Analyses and specializes in robotics and Russia.
Mr. Bennett has over 15 years of program and project management experience with US government, Department of Defense and private sector. Currently conducting research on the Russian defense and security issues, as well as the emergence of unmanned warfare. Formerly an Assistant Research Fellow at the National Defense University, working on emerging and disruptive technology for the Department of Defense policy development and concept of operations.
In today’s digital age, “Data” is the new oil. There is a huge amount of data around us, and it’s expanding at an exponential rate. The challenge is that this big data set (Big Data) is noisy and heterogeneous. So, it’s very important to extract knowledge or insights from the data around us.
The field of data science explores the patterns within large data sets and aims to drive meaningful actionable decisions.
Data science is an umbrella term that encompasses data analytics, data mining, machine learning (ML), artificial intelligence (AI), and several other related disciplines.
In this video, learn about the top 5 countries to study data science, ML, AI & big data analytics abroad. Additionally, get to know the top universities, job prospects, and average salaries in those countries.
Top Universities for Masters (MS) in Data Science in USA
Top Masters in Data Science and Analytics Programs in Canada
Masters in Data Science, ML and Analytics in UK: Top Universities, Costs, Job Prospects, Salaries
MS Machine Learning / AI vs MS Data Science vs MS Business/Data Analytics – How to Choose the Right Program
Masters in Data Science and Analytics in Germany – Top Universities, Jobs & Salaries
Masters in Data Science and Business Analytics in France: Best Programs, Jobs & Salaries
Masters (MS) Business Analytics in Canada: Top Schools, Skills, Jobs and Salaries
Best Master’s (MS) in Analytics Programs in USA
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#DataScience #BigData #BusinessAnalytics #MachineLearning #ArtificialIntelligence
To book science and technology speaker Max Tegmark for your next event, please contact Speakers@SPEAKING.COM. And for more information about Max Tegmark (including expanded bio, program outlines, more videos, speaking fees, etc.) see: https://speaking.com/speakers/max-tegmark/
MIT professor Max Tegmark is known for his out-of-the-box ideas and decades of contributions to physics, cosmology, and AI. As the cofounder of the Future of Life Institute, he brings together leading actors in AI research, including Elon Musk and Larry Page, to explore the development and potential outcomes of super intelligent machines and how we can reap the benefits of them while avoiding the pitfalls.
Dr. Tegmark is dedicated to making sure that as AI evolves, that it works to better all of society. His current research focuses on creating AI that can be trusted via engineering systems of reasoning that can be easily followed and understood.
Dr. Tegmark is the author of over 200 scientific papers, twelve of which have been cited over 500 times. He has published two best-selling books, Our Mathematical Universe and Life 3.0: Being Human in the Age of Artificial Intelligence. Furthermore, Dr. Tegmark has been interviewed in numerous documentaries including Do You Trust This Computer, Parallel Worlds, Parallel Lives, and several episodes of Sci Fi Science: Physics of the Impossible.
Much of his early work focuses on astrophysics, specifically cosmic precision, new technology for low-frequency radio interferometry, and theories that describe the universe as a mathematical structure and possible multiverses. He was elected Fellow of the American Physical Society in 2012 and chosen as the winner of Science magazine’s “Breakthrough of the Year: 2003.”
For more information about Max Tegmark: https://speaking.com/speakers/max-tegmark/
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Science Documentary: Genetics, Robotics, Quantum Computing, Artificial Intelligence
There are several technologies emerging that will change what it means to be called a human being. Genetics is the manipulation of cells at their most basic levels. Along with genomics, the manipulation of proteins will give scientists the ability to create whatever they want.
In the pharmaceutical industry, one of the most exciting treatments to arise are memory pills. These should be available in the coming years to treat patients with alzheimer’s and eventually the general public. The possibilities include eventually being able to take a pill to help you learn another language, improve test taking, etc.
Another interesting promise of the future of genetics is the promise of various new vaccines to help quit smoking. Also, DARPA has developed an anti-sleep vaccine. This is a vaccine that simply shuts off the trigger that makes us fall asleep. Another human enhancement technology being developed by DARPA is a drug that triggers the body to burn fat cells.
The future of robotics involves the melding of human and machine attributes into what can be referred to as a cyborg. Intelligent machines are becoming more and more prevalent in military combat. And they are also becoming much more complex. Wearable robot suits that amplify your lifting capacity will be available in the not so distant future.
Consciousness and Neuroscience
One of the biggest mysteries in neuroscience is consciousness. Scientists have discovered a lot about the brain, but little progress has been made in understanding consciousness. It is as if the study of the brain and mind are separate. It is puzzling to scientists as to why we subjectively experience anything. If we understood every single brain cell and every single brain process, we still would not understand consciousness as a physical object within the brain. Any kind of theory of the brain, that is able to handle and support an understanding of subjective experience or neuro-rehabilitation or other such complex brain functions may revolutionize artificial intelligence and robotics.
AGI – Artificial General Intelligence
Artificial intelligence is currently being used to control animated characters in video games. Integrating Artificial intelligence into robotics has proved rather difficult. As humans, we use metaphors everyday in our speech to describe complex ideas. Trying to teach robots to be able to interpret and comprehend these metaphors could be a tough task. But as scientists progress in their integration of artificial intelligence and robotics, we may soon see more and more of our human senses replicated in a much more precise and sophisticated fashion.
Optical Quantum Computing
The future of medial treatment may involve the use of quantum computers. Patients will have a device like a cell phone that they carry with them which monitors their health and sends the information to their doctor who can then prescribe treatment to his patient. Understanding quantum physics has allowed researchers to develop this technology as well as other more and more complex quantum technologies.
Science Documentary: Personalized Medicine, Synthetic Biology , a documentary on genetic design
Science Documentary:Perfect lenses,smart textiles,biomedical sensors a documentary on nanotechnology
Science Documentary: Comets: threat to extinguish life and potential to bring life: Rosetta, Philae
Science Documentary: The Sun, a science documentary on star life cycles, star formation
Science Documentary: Big Bang, Inflation, Multiverse, a Documentary on Cosmology
Science Documentary: Planet formation, a documentary on elements, early earth and plate tectonics
Science Documentary : Electromagnetic Spectrum , a science documentary on forms of light
This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines.
– Natural Language Processing (Part 1): Introduction to NLP & Data Science
– Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python
– Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python
– Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python
– Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python
– Natural Language Processing (Part 6): Text Generation with Markov Chains in Python
All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial
For downloadable versions of these lectures, please go to the following link:
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
Interview with Stuart Russell, Professor of Computer Science, University of California, Berkeley, at the AI for Good Global Summit 2018, ITU, Geneva, Switzerland.
Prof Dr Prof h.c. Andreas Dengel (Scientific Director of the Smart Data & Knowledge Services Research Department, DFKI) is interviewed by Dr Nabil Alsabah (Head of Artificial Intelligence, Bitkom) about “AI: The Blurring Line Between Reality and Science Fiction” at hub.berlin on April 10th 2019.
hub.berlin is one of the most important technology and business festivals for digital movers and makers in Europe. More than 8,000 digital experts experience the future of digitisation today. Managers, founders, politicians and scientists come together to shape the digital transformation.
Discover more at www.hub.berlin.
#hubberlin #interview #bas19
“The very first idea that we might be suffering this pandemic came from an AI program.”
Artificial intelligence (AI) can be used in testing, tracing and even treating COVID-19. Hear more from Professor Toby Walsh in this episode all about coronavirus and the computer.
This is episode 20 of the #LatestFromScience series, our response to provide credible and up to date information right now on COVID-19. You can watch more episodes here: https://www.youtube.com/playlist?list=PL9DfJTxCPaXKZiJ1cZioIhxmJZmwfru_o
This is also a chance for you to ask your questions. Send us a direct message via Facebook, Tweet us on the hashtag #LatestFromScience, or visit our website, science.org.au/covid19
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Machine Learning for Engineering and Science Applications – Intro Video
In this talk, I will introduce the audience to the emerging area of
computational social science, focusing on how machine learning for social science differs from machine learning in other contexts. I will present two related models — both based on Bayesian Poisson tensor decomposition — for uncovering latent structure from count data. The first is for uncovering topics in previously classified government documents, while the second is for uncovering multilateral relations from country-to-country interaction data. Finally, I will talk briefly about the broader ethical implications of analyzing social data.
Hanna Wallach is a Senior Researcher at Microsoft Research New York City and an Adjunct Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. She is also a member of UMass’s Computational Social Science Institute. Hanna develops machine learning methods for analyzing the structure, content, and dynamics of social processes. Her work is inherently interdisciplinary: she collaborates with political scientists, sociologists, and journalists to understand how organizations work by analyzing publicly available interaction data, such as email networks, document collections, press releases, meeting transcripts, and news articles. To complement this agenda, she also studies issues of fairness, accountability, and transparency as they relate to machine learning. Hanna’s research has had broad impact in machine learning, natural language processing, and computational social science. In 2010, her work on infinite belief networks won the best paper award at the Artificial Intelligence and Statistics conference; in 2014, she was named one of Glamour magazine’s “35 Women Under 35 Who Are Changing the Tech Industry”; in 2015, she was elected to the International Machine Learning Society’s Board of Trustees; and in 2016, she was named co-winner of the 2016 Borg Early Career Award. She is the recipient of several National Science Foundation grants, an Intelligence Advanced Research Projects Activity grant, and a grant from the Office of Juvenile Justice and Delinquency Prevention. Hanna is committed to increasing diversity and has worked for over a decade to address the underrepresentation of women in computing. She co-founded two projects—the first of their kind—to increase women’s involvement in free and open source software development: Debian Women and the GNOME Women’s Summer Outreach Program. She also co-founded the annual Women in Machine Learning Workshop, which is now in its eleventh year. Hanna holds a BA in computer science from the University of Cambridge, an MSc in cognitive science and machine learning from the University of Edinburgh, and a PhD in machine learning from the University of Cambridge.
Links to materials and sites referenced in Hanna’s talk:
See the complete SciPy 2016 Conference talk & tutorial playlist here: https://www.youtube.com/playlist?list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6.
Some chronic conditions, such as the autoimmune disease scleroderma, are especially difficult to treat because patients exhibit highly variable symptoms, complications and treatment responses. The process of finding an effective treatment for an individual can be frustrating for doctors, and painful and expensive for patients.
With support from the National Science Foundation (NSF), computer scientist and professor Suchi Saria, with Dr. Fredrick Wigley and an interdisciplinary team of experts at Johns Hopkins University, is leading a groundbreaking effort using Big Data to ease some of that pain for scleroderma patients. The team’s research is in machine learning, a subfield of computer science and statistics that allows machines to learn from data. The team designs statistical algorithms that enable computers to analyze large volumes of medical records and identify subgroups of patients with similar patterns of disease progression.
In addition, the system learns the symptoms and treatments that are predictive of specific patterns of improvement or deterioration to help doctors pick the right set of treatments for an individual patient. Doctors can then map the course of treatment for new patients, based in part on what the computers reveal about what happened to other patients with similar symptoms.
Saria foresees data analysis similar to this helping clinicians treat other chronic diseases, such as lupus and rheumatoid arthritis.
Research in this episode was supported by NSF award #1418590, Smart and Connected Health (SCH)/Integrative Projects (INT): Collaborative Research: Modeling Disease Trajectories in Patients with Complex, Multiphenotypic Conditions.
NSF Grant URL: http://www.nsf.gov/awardsearch/showAward?AWD_ID=1418590&HistoricalAwards=false
Miles O’Brien, Science Nation Correspondent
Ann Kellan, Science Nation Producer
A Stationers’ Company Digital Media Group event at Stationers’ Hall on ‘Artificial Intelligence – Our Future: The Impact on the Content and Communication Industries’
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Physicist Max Tegmark on predictions that cannot be observed, explanation of Universe’ fine tuning, and quantum computer
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“Human-compatible AI” by Stuart Russell, Professor of Computer Science at UC Berkeley on March 21st 2018 at the Data Driven Paris.
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** Data Science Certification using R: https://www.edureka.co/data-science-r-programming-certification-course **
In this video on Text Mining In R, we’ll be focusing on the various methodologies used in text mining in order to retrieve useful information from data. The following topics are covered in this session:
(01:18) Need for Text Mining
(03:56) What Is Text Mining?
(05:42) What is NLP?
(07:00) Applications of NLP
(08:33) Terminologies in NLP
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
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#textmining #textminingwithr #naturallanguageprocessing #datascience #datasciencetutorial #datasciencewithr #datasciencecourse #datascienceforbeginners #datasciencetraining #datasciencetutorial
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About the Course
Edureka’s Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on ‘R’ capabilities.
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Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the ‘Roles’ played by a Data Scientist
2. Analyze Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyze data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
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Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a ‘Data Scientist’
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. ‘R’ professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies.
For online Data Science training, please write back to us at email@example.com or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
Discover the best data science startups to work for in 2020… or read the full article: http://bit.ly/38y6GN6
Before you start sending out your resume to Bain and McKinsey, consider our list of the Best Data Science Startups to Work For in 2020!
Why work for a data science startup?
Sure, big data science consultancies have the stability and the benefits every aspiring data scientist strives for. However, you may find yourself working on predictable and often repetitive tasks with little opportunities for growth. At least for the first few years.
Startups, on the other hand, allow you to develop your skillset by trying new things and handling a variety of challenges. Responsibilities there change quite frequently. So, within less than a year you could be doing something entirely different… And a lot more interesting for you than what you were initially hired for. In other words, the sky is the limit!
We based our research on insight from KDnuggets, LinkedIn, and Forbes to discover the best startups to watch in 2019. Then we picked our top 10 – the fastest growing machine learning and tech startup companies that give you both development opportunities, and a chance to work on inspiring projects.
So… watch our data science startups review to learn what they do, where to apply, and why you should consider working there. And don’t mind the order – these startups are so unique, that every single one of them could easily be number 1 on our list.
LINK TO OUR ULTIMATE GUIDE TO STARTING A CAREER IN DATA SCIENCE
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Le deep learning, une technique qui révolutionne l’intelligence artificielle…et bientôt notre quotidien !
Écrit et réalisé par David Louapre © Science étonnante
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La vidéo de Fei Fei Li à TED : https://www.ted.com/talks/fei_fei_li_how_we_re_teaching_computers_to_understand_pictures
La leçon inaugurale de Yann Le Cun au Collège de France : http://www.college-de-france.fr/site/yann-lecun/inaugural-lecture-2016-02-04-18h00.htm
Russakovsky, Olga, et al. « Imagenet large scale visual recognition challenge. » International Journal of Computer Vision 115.3 (2015): 211-252. http://arxiv.org/pdf/1409.0575
Radford, Alec, Luke Metz, and Soumith Chintala. « Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. » arXiv preprint arXiv:1511.06434 (2015). http://arxiv.org/pdf/1511.06434
Zeiler, Matthew D., and Rob Fergus. « Visualizing and understanding convolutional networks. » Computer vision–ECCV 2014. Springer International Publishing, 2014. 818-833. http://arxiv.org/pdf/1311.2901
Vinyals, Oriol, et al. “Show and tell: A neural image caption generator.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. http://arxiv.org/pdf/1411.4555.pdf
Joscha Bach presents his talk “Artificial General Intelligence as a Foundational Discipline in Cognitive Science” at the Seventh Conference on Artificial General Intelligence (AGI-14) in Quebec City (http://www.agi-conference.org/2014) as part of the Special Session on AGI and Cognitive Science.
Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora.
Python for Data Science Certification Training Course: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Data-Science-NLP-6WpnxmmkYys&utm_medium=Tutorials&utm_source=youtube
Download the Machine Learning Career Guide to explore and step into the exciting world of Machine Learning, and follow the path towards your dream career- https://www.simplilearn.com/machine-learning-career-guide-pdf?utm_campaign=Data-Science-NLP-6WpnxmmkYys&utm_medium=Tutorials&utm_source=youtube
The Data Science with Python course is designed to impart an in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The course is packed with real-life projects, assignment, demos, and case studies to give a hands-on and practical experience to the participants.
Mastering Python and using its packages: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization.
Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it’s modeling, and implementation using SAS.
As a part of the course, you are provided with 4 real-life industry projects on customer segmentation, macro calls, attrition analysis, and retail analysis.
Who should take this course?
There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals:
1. Analytics professionals who want to work with Python
2. Software professionals looking for a career switch in the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in Analytics and Data Science
5. Experienced professionals who would like to harness data science in their fields
6. Anyone with a genuine interest in the field of Data Science
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