** Machine Learning Training with Python: https://www.edureka.co/machine-learning-certification-training ** This Edureka video on ‘Mathematics for Machine Learning’ teaches you all the math needed to get started with mastering Machine Learning. It teaches you all the necessary topics and concepts of Linear Algebra, Multivariate Calculus, Statistics, and Probability and also dives into the actual implementation of these topics. Blog Link: https://bit.ly/2PX5lIp Check out our playlist for more videos: http://bit.ly/2taym8X —————————————————————————— Subscribe to our channel to get video updates: https://bit.ly/2PYu1jD Hit the subscribe button above. Edureka Community: https://bit.ly/EdurekaCommunity Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekaIN LinkedIn: https://www.linkedin.com/company/edureka/ SlideShare: https://www.slideshare.net/EdurekaIN/ #Edureka #MachineLearning #MathematicsForMachineLearning # —————————————————————————— How does it work? 1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you with a Grade and a Verifiable Certificate! —————————————————————————— About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in Python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You [More]
Free access Closer to Truth’s library of 5,000 videos: http://bit.ly/2UufzC7 Mathematics describes the real world of atoms and acorns, stars and stairs, with remarkable precision. So is mathematics invented by humans just like chisels and hammers and pieces of music? Or is mathematics discovered—always out there, somewhere, like mysterious islands waiting to be found? Whatever mathematics is will help define reality itself. Watch more interviews with Max Tegmark: http://bit.ly/2U4lCga Watch more interviews on mathematics: http://bit.ly/2mBwNkm Join our Facebook community: https://www.facebook.com/CloserToTruthTV Follow us on Twitter: https://twitter.com/CloserToTruth Closer To Truth presents the world’s greatest thinkers exploring humanity’s deepest questions. Discover fundamental issues of existence. Engage new and diverse ways of thinking. Appreciate intense debates. Share your own opinions. Seek your own answers.
Do you need to know math to do machine learning? Yes! The big 4 math disciplines that make up machine learning are linear algebra, probability theory, calculus, and statistics. I’m going to cover how each are used by going through a linear regression problem that predicts the price of an apartment in NYC based on its price per square foot. Then we’ll switch over to a logistic regression model to change it up a bit. This will be a hands-on way to see how each of these disciplines are used in the field. Code for this video (with coding challenge): https://github.com/llSourcell/math_of_machine_learning Please Subscribe! And like. And comment. That’s what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Sign up for the next course at The School of AI: http://theschool.ai/ More learning resources: https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568 https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/ https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning https://courses.washington.edu/css490/2012.Winter/lecture_slides/02_math_essentials.pdf Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Max Tegmark has a proud reputation as an unconventional thinker. In this presentation, Professor Tegmark talks about his famous concept of the multiverse and his view that reality is a mathematical entity. And he makes this material very interesting and accessible to a wide audience –no expertise required. Max Tegmark was raised in Sweden. He received his two bachelor degrees at different universities there, one in Physics from the Royal Institute of Technology, and the other in Economics at the Stockholm School of Economics. He then moved to University of CA Berkeley for his graduate work and received his PhD there in 1994. He was a research associate with the Max-Planck-Institut für Physik in Munich, then a Hubble Fellow and a member of the Institute for Advanced Study, Princeton University. He joined the faculty of the University of Pennsylvania, but then was lured away by the MIT physics department, where he has been since 2004. Dr. Tegmark has made major contributions to physics/cosmology, some of which he discusses in this presentation. He is a Fellow of the American Physical Society and has received numerous awards.
This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future. Who is this class for: This class is for people who would like to learn more about machine learning techniques, but don’t currently have the fundamental mathematics in place to go into much detail. This course will include some exercises that require you to work with code. If you’ve not [More]