Introduction :
The world is full of big data, including photos, music, videos, text, etc. It grows continuously day by day, and it never ends or never ends, as you might collect. This data is not only produced by humans, but also by computers, phones, and other devices. Millions and millions of drives, spreadsheets, etc. to maintain this data, but we do not have much idea of what to expect or how to use this data effectively but efficiently. So, this blog helps you to understand what is machine learning, types of machine learning and top ML influencers.
What is Machine Learning?
Machine Learning (ML) is an artificial intelligence (AI) program that gives systems the ability to automatically learn and develop from experience without being programmed. Machine learning focuses on the development of computer programs that can access data and use it for self-study.
Machine Learning Models can be broadly divided into 3 buckets
Supervised Machine learning models: Such models are made up of data with a target column. Examples: Line Down, Descent, SVM, KNN, Naive Bayes.
Unsupervised Machine Learning models: Such models use incoming data in the target column. Examples: Clustering algorithms – K Means, Hierarchical clustering, Apriori
Strengthening learning models: Such models learn with knowledge and develop each time after making a mistake. Examples: Markov’s Decision Process and Q Readings
Who are Machine Learning Influencers?
Machine Learning specialists or influencers are a specialist in developing machine learning, a branch of computer science that focuses on developing algorithms that can “read” or adapt to data and make predictions.
Top Ten Machine Learning Influencers to look our for in 2022 :
1) Vladimir Vapnik –
Vladimir Vapnik was born on 6 December 1936 in the Soviet Union. He studied in the Institute of Control Sciences, the Russian Academy of Sciences and Uzbek State University. He is popularly known for Vapnik–Chervonenkis theory, Vapnik–Chervonenkis dimension, Support-vector machine, Statistical learning theory, Structural risk minimization. In 2003 he was awarded by Alexander Humboldt Research Award, In 2005 he got Gabor Award, International Neural Network Society, He received the National Academy of Engineering in 2006, In the year 2008 Vladimir Vapnik was awarded by Paris Kanellakis Award, In 2010 he received IEEE Neural Networks Pioneer Award, In 2012 he got IEEE Frank Rosenblatt Award, He received C&C Prize in 2013, In the year 2014 Vapnik got Kampé de Fériet Award, he got IEEE John von Neumann Medal in 2017 and 2018 he received Kolmogorov Medal. Vladimir Naumovich Vapnik is one of the great developers of Vapnik – Chervonenkis’ mathematical theory, co-founder of the support vector machine method, and the support-vector integration algorithm.
2) Geoffrey Everest Hinton:
Geoffrey Everest Hinton was born on 6 December 1947 in Wimbledon, London, England. He completed a BA from the University of Cambridge and a PhD from the University of Edinburgh. He is popularly known for Applications of Backpropagation, Boltzmann machine, Deep learning and Capsule neural network. Geoffrey Everest Hinton CC FRS FRSC is a British-Canadian psychiatrist and computer scientist, best known for his work on artificial intelligence networks. Since 2013, he has spent his time working for Google and the University of Toronto. In the year 1990 he was awarded by AAAI Fellow, In 2001 he got Rumelhart Prize, In 2005 he received IJCAI Award for Research Excellence, He was awarded by IEEE Frank Rosenblatt Award, In the year 2016 he got James Clerk Maxwell Medal, Same year in 2016 he received BBVA Foundation Frontiers of Knowledge Award and In 2018 he got Turing Award.
3) Bernhard Schölkopf:
Bernhard Schölkopf was born on 20 February in the year 1968, He completed MSc in mathematics from the University of London, He did a diploma in physics from the University of Tübingen, Completed a PhD in computer science from the Technical University of Berlin. He is a Computer Scientist from Germany he is popularly known for his work in ML Machine learning, especially of kernel methods and causality. He got J. K. Aggarwal Prize of the International Association for Pattern Recognition in 2006, In 2011 he was awarded by Max Planck Research Award, In 2012 he got the Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities, He got Milner Award in the year 2014, In 2017 he received Member of the German National Academy of Science (Leopoldina), Bernhard Schölkopf got Fellow of the ACM (Association for Computing Machinery) in 2018 same year in 2018 he also got Leibniz Prize, In the year 2019 he receives Körber European Science Prize Causality in Statistics Education Award, American Statistical Association, In 2020 he was awarded by BBVA Foundation Frontiers of Knowledge Awards.
4) Andrew Yan-Tak Ng :
Andrew Yan-Tak Ng was born on 18 April 1976 in the United Kingdom. He completed his high school at Raffles Institution, He studied BS from Carnegie Mellon University, He completed MS from Massachusetts Institute of Technology and he did PhD from the University of California, Berkeley. He is famously known for Machine learning, Deep Learning, massive open online course and Educational technology. In 2007 he got Sloan Fellowship, In the year 2008 he was awarded by MIT Technology Review TR35, He got IJCAI Computers and Thought Award in 2009. He received Time 100 Most Influential People in 2013, In 2013 he was honoured Fortune’s 40 Under 40, He received CNN 10 in 2013, He got Fast Company’s Most Creative People in Business and 2015 he was awarded by World Economic Forum Young Global Leaders.
5) Christopher Michael Bishop:
Christopher Michael Bishop was born on 7 April 1959 in Norwich, He completed a BA from the University of Oxford and completed a PhD from the University of Edinburgh, He is popularly known as Pattern Recognition and Machine Learning book. In 2008 he received Royal Institution Christmas Lectures and In 2010 he got the Turing Lecture award.
6) Fei-Fei Li:
Fei-Fei Li was born in the year 1976 she completed B.A. in physics from Princeton University and she completed PhD in 2005 from the California Institute of Technology. She is popularly known for her works Computer Vision, Machine Learning, Artificial Intelligence, AI and Healthcare, Cognitive neuroscience. She was awarded by Paul and Daisy Soros Fellowship for New Americans in the year 1999, In 2006 she got Microsoft Research New Faculty Fellowship, In 2011 she received Sloan Fellowship, In 2016 she got One of the 40 “The great immigrants,” Carnegie Foundation same year in 2016 she was awarded by J.K. Aggarwal Prize International Association for Pattern Recognition (IAPR), In the year 2018 she got ACM Fellow for “contributions in building large knowledge bases for machine learning and visual understanding”, In 2020 Fei-Fei Li got National Academy of Medicine also same year in 2020 she was Elected to National Academy of Engineering.
7) Yann André LeCun :
Yann André LeCun was born on 8 July in year 1960 in Soisy-sous-Montmorency, France . He completed his MSc from ESIEE Paris. He did his PhD at Pierre and Marie Curie University now Sorbonne University. He is a Computer Scientists from French he works in fields like computer vision, mobile robotics, machine learning and computational neuroscience, He is known for his work for Deep Learning in 2018 he was awarded by Turing Award, In 2019 he got a Fellowship of the Association for the Advancement of Artificial Intelligence, In 2020 he was honoured by Legion of Honour.
8) Zoubin Ghahramani:
Zoubin Ghahramani was born on 8 February 1970 in Iran. He studied at the University of Pennsylvania and from the Massachusetts Institute of Technology. He is popularly known for his work Graphical models, Variational Bayes and Computational neuroscience. He is a British-Iranian researcher and also a professor Information Engineering University of Cambridge. His scientific career fields are Machine learning and Bayesian statistics. In 2015 he was awarded by Fellowship of the Royal Society (FRS).
9) Jürgen Schmidhuber :
Jürgen Schmidhuber was born on 17 January 1963 in Munich, West Germany. His nationality is German. He studied at the Technical University of Munich. He is a computer scientist and popularly known for his works like Artificial intelligence, Deep Learning, artificial curiosity, meta-learning, artificial neural networks, Gödel machine and recurrent neural networks. His scientific career field is Artificial Intelligence from Institute Dalle Molle Institute for Artificial Intelligence Research. He is also co-director of Dalle Molle Institute for Artificial Intelligence Research located in Lugano, Ticino, Southern Switzerland. In 2013 he got the Helmholtz Award of the International Neural Network Society, In 2016 he was awarded the Neural Networks Pioneer Award of the IEEE Computational Intelligence Society.
10) David Meir Blei: David Meir Blei is from the United States, He is a professor at Columbia University in Statics and Computer Science departments. He is popularly known for Topic models. In the year 2015 he was awarded by Presidential Early Career Award for Scientists and Engineers (PECASE) also got Association for Computing Machinery ACM Fellow. His scientific career field is Artificial Intelligence of Institutions Princeton University and Columbia University. He studied B.S. from Brown University in the year 1997 and completed PhD from the University of California, Berkeley in 2004.
Conclusion :
Recent years have shown that machine learning can be used to perform various automated tasks that are considered to be the only activities that people may like, such as visual imagery, text production or gameplay. ahead in the world. But Google’s DeepMind has proved to be incorrect. They have shown that they can learn the movements that need to be considered even in the most difficult game such as walking machines. There has been a lot of progress in the gaming industry like DotaBot from the OpenAI team.
Machine learning will have a huge impact on the economy and health in general. All workflow and industry can be automated and the job market is permanently changed.
If you want to start learning about machine learning this is the right time because machine learning engineers are important. After all, many companies need to get into the door of machine learning and practical skills. Now as machine learning requires all attention, it is up to us engineers and researchers to drive to achieve greater success in the field of machine learning.
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