Title: Break Into AI: A Q&A with Andrew Ng on Building a Career in Machine Learning
Speaker: Andrew Ng
Date: 12/4/2018

Abstract
Andrew Ng will share tips and tricks on how to break into AI. He will discuss some of the most valuable skills for today's machine learning engineers, how to gain the experience to successfully switch careers, and how to build a habit of lifelong learning. He will also take questions from aspiring engineers and business professionals who want to work on AI-powered products.

SPEAKER
Andrew Ng, General Partner, AI Fund; CEO, Landing AI;
Adjunct Professor, Stanford University
Dr. Andrew Ng, a globally recognized leader in AI, is a General Partner at AI Fund and CEO of Landing AI. As the former Chief Scientist at Baidu and the founding lead of Google Brain, he led the AI transformation of two of the world’s leading technology companies. A longtime advocate of accessible education, Dr. Ng is the Co-founder of Coursera, an online learning platform, and founder of deeplearning.ai, an AI education platform. Dr. Ng is also an Adjunct Professor at Stanford University’s Computer Science Department.

MODERATOR
Juan Miguel de Joya, UN ITU; ACM Practitioners Board
Juan Miguel de Joya is the in-house consultant for Artificial Intelligence and Emerging Technologies at the United Nations International Telecommunications Union. Prior to this role, he served as a contractor at Facebook/Oculus and Google, worked at Pixar Animation Studios and Walt Disney Animation Studios, and was an undergraduate researcher in graphics at the Visual Computing Lab at the University of California, Berkeley. In his spare time, he is part of the ACM Practitioners Board, the ACM Professional Development Committee, and the ACM SIGGRAPH Strategy Group. His current interests include artificial intelligence, computer vision, mixed reality, computational physics, the web, and the human impact of computing in society at large.

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: Eric Grimson

In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors.

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

One of the 9 films available in Successful Learners

How does my brain work?
What happens in my brain when Im learning?
What stops my brain from learning?
What can I do to develop a learning brain?

Today we’re going to teach John Green Bot how to tell the difference between donuts and bagels using supervised learning! Supervised learning is the process of learning WITH training labels, and is the most widely used kind of learning with it comes to AI - helping with stuff like tagging photos on Facebook and filtering spam from your email. We’re going to start small today and show how just a single neuron (or perceptron) is constructed, and explain the differences between precision and recall. Next week, we'll build our first neural network.

Read more about the perceptron and update rule here: https://jontysinai.github.io/j....ekyll/update/2017/11

Crash Course is produced in association with PBS Digital Studios.
https://www.youtube.com/pbsdigitalstudios

Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse

Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:

Eric Prestemon, Sam Buck, Mark Brouwer, Timothy J Kwist, Brian Thomas Gossett, Haxiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Zach Van Stanley, Bob Doye, Jennifer Killen, Nathan Catchings, Brandon Westmoreland, dorsey, Indika Siriwardena, Kenneth F Penttinen, Trevin Beattie, Erika & Alexa Saur, Justin Zingsheim, Jessica Wode, Tom Trval, Jason Saslow, Nathan Taylor, Khaled El Shalakany, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, David Noe, Shawn Arnold, William McGraw, Andrei Krishkevich, Rachel Bright, Jirat, Ian Dundore
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#CrashCourse #ArtificialIntelligence #MachineLearning

In this video having Learner License Test Question with objective type bits. Each question having 4 options.

Music :
Artist : Bensound
Song : bensound-november
Website : http://www.bensound.com/
This Music used in this video is from Bensound.
You can get more Music from Bensound.

How can we tell if a drink is beer or wine? Machine learning, of course! In this episode of Cloud AI Adventures, Yufeng walks through the 7 steps involved in applied machine learning.

The 7 Steps of Machine Learning article: https://goo.gl/XEo6i2

Watch more episodes of AI Adventures here: https://goo.gl/UC5usG

TensorFlow Playground: http://playground.tensorflow.org
Machine Learning Workflow: https://goo.gl/SwLnSz
Hands-on intro level lab Baseline: Data, ML, AI → http://bit.ly/2KoBF6Y
Qwiklabs: https://goo.gle/2RH89Kh

Want more machine learning? Subscribe to the channel: https://goo.gl/S0AS51

How getting something completely wrong can actually help you out. Professor Uwe Aickelin explains anti-learning.

Could We Ban Encryption?: https://youtu.be/ShUyfk4QB-8
XOR and the Half Adder: https://youtu.be/VPw9vPN-3ac
The Singularity & Friendly AI: https://youtu.be/uA9mxq3gneE
Machine Learning Methods: https://youtu.be/qDbpYUbf3e0

http://www.facebook.com/computerphile
https://twitter.com/computer_phile

This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: http://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

New technologies have fundamentally changed the way we make and experience music. In this session Claire Evans, artist, author and one half of the pop duo YACHT talks about deep learning as a tool in their creative process. Their new album explores Google AI’s research project, Magenta, an open-source music-making package using machine learning models.

Watch more #io19 here:
Inspiration at Google I/O 2019 Playlist → https://goo.gle/2LkBwCF
Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions
Learn more on the I/O Website → https://google.com/io

Subscribe to the Google Developers Channel → https://goo.gle/developers
Get started at → https://developers.google.com/

Speaker(s): Claire Evans, Adam Roberts, Jesse Engel

T1AFC0

MIT 6.034 Artificial Intelligence, Fall 2010
View the complete course: http://ocw.mit.edu/6-034F10
Instructor: Patrick Winston

In this lecture, we explore support vector machines in some mathematical detail. We use Lagrange multipliers to maximize the width of the street given certain constraints. If needed, we transform vectors into another space, using a kernel function.

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

I always sucked at baseball... until now... ok, I still probably suck. Go to https://NordVPN.com/MarkRober and use code MARKROBER to get 75% off a 3 year plan and an extra month for free.

Go subscribe to Jabril's channel!!! https://www.youtube.com/channe....l/UCQALLeQPoZdZC4JNU

Simple app (it's actually just a webpage for now): http://jabrils.com/sp/
Complex app using ML: https://github.com/Jabrils/Unc....le-Rober-Baseball-Pr

CORRECTION: I said in ad portion of the video that my data could be stolen on unprotected WiFi. This is true but in the example I gave with YouTube which uses HTTPS it’s already encrypted. Sorry. Didn’t know that’s how it worked. I will correct that in any future ads with Nord.



***Music***
0:29 Dansez - Fasion https://www.epidemicsound.com/track/QXxs7iZ3Rn
1:01 Dive - Lvly https://www.epidemicsound.com/track/4JmHD4z5Bj
2:19 Dansez - Fasion https://www.epidemicsound.com/track/QXxs7iZ3Rn
4:16 Kalimba Jam - Blue Wednesday https://soundcloud.com/bluewednesday/
5:23 Take Me Out to the Ballgame -Matt Cherne- https://smarturl.it/einsteinbeats
7:04 Arrow - Andrew Applepie http://andrewapplepie.com/
8:18 Cereal Killa - Blue Wednesday https://soundcloud.com/bluewednesday/
11:16 Salamanca - Sarah, the Illstrumentalist https://www.epidemicsound.com/track/nAhVeSoknF
13:12 Q - Blue Wednesday https://soundcloud.com/bluewednesday/
14:18 Too Happy to be cool - Notebreak https://soundcloud.com/notebre....ak/dubstep-too-happy



Summary: I wanted to see if I could make an app that could decode baseball signs. Turns out we could and it was a great opportunity for me to learn more about Machine Learning and neural networks and artificial intelligence from my friend Jabril.



MERCH-
They are soft- https://teespring.com/stores/markrober


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I make videos like this once a month all year long while supplies last:

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Gary Marcus is a professor emeritus at NYU, founder of Robust.AI and Geometric Intelligence, the latter is a machine learning company acquired by Uber in 2016. He is the author of several books on natural and artificial intelligence, including his new book Rebooting AI: Building Machines We Can Trust. Gary has been a critical voice highlighting the limits of deep learning and discussing the challenges before the AI community that must be solved in order to achieve artificial general intelligence. This conversation is part of the Artificial Intelligence podcast.

INFO:
Podcast website:
https://lexfridman.com/ai
iTunes:
https://apple.co/2lwqZIr
Spotify:
https://spoti.fi/2nEwCF8
RSS:
https://lexfridman.com/category/ai/feed/
Full episodes playlist:
https://www.youtube.com/playli....st?list=PLrAXtmErZgO
Clips playlist:
https://www.youtube.com/playli....st?list=PLrAXtmErZgO

EPISODE LINKS:
Gary website: http://garymarcus.com/
Rebooting AI book: https://amzn.to/2pAPcz3
Kluge book: https://amzn.to/2ncvR6o

OUTLINE:
0:00 - Introduction
1:37 - Singularity
5:48 - Physical and psychological knowledge
10:52 - Chess
14:32 - Language vs physical world
17:37 - What does AI look like 100 years from now
21:28 - Flaws of the human mind
25:27 - General intelligence
28:25 - Limits of deep learning
44:41 - Expert systems and symbol manipulation
48:37 - Knowledge representation
52:52 - Increasing compute power
56:27 - How human children learn
57:23 - Innate knowledge and learned knowledge
1:06:43 - Good test of intelligence
1:12:32 - Deep learning and symbol manipulation
1:23:35 - Guitar

CONNECT:
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This is a talk for people who know code, but who don’t necessarily know machine learning. Learn the ‘new’ paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code. This session will guide you through understanding many of the new concepts in machine learning that you might not be familiar with including eager mode, training loops, optimizers, and loss functions.

Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol
TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM
Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions
Learn more on the I/O Website → https://google.com/io

Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1
Get started at → https://www.tensorflow.org/

Speaker(s): Laurence Moroney and Karmel Allison

T700B4

Alphabets, the building blocks of literacy. Here is a video especially designed to make it easy for your child to recognize these fundamental characters. It's delightfully animated! Let's begin and let's learn alphabets.

Learn ABC - 00:04
My ABC - 02:16
Write Big ABC - 06:35
Write small abc - 10:25
ABC Song - 14:23
Animal Alphabet song - 14:56
Alphabet Train - 17:59

Our YouTube Channels:
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Dr. Conor McDonough Quinn is a documentary linguist, endangered language revitalization worker, and avid learner and teacher of languages. Raised in Portland, Maine, he has lived at length in Indonesia, China, and Oman, and has worked extensively with several indigenous communities of the Northeast. Learning his own family's endangered heritage language (hint: look at the name) in his early teens gave him a passion for helping others to do the same, and led him to pursue linguistics—somewhat single-mindedly—throughout his academic career. Alongside a hefty set of technical research questions in linguistics, he is currently most interested in developing tools to help speech communities carry out their own linguistic documentation and revitalization work, and in finding new ways to reduce the barriers to learning new languages.

In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)

Download this track here: http://www.cdbaby.com/cd/michaelsealey
CD Baby: ► https://store.cdbaby.com/Artist/MichaelSealey
Downloads on iTunes: ► https://itunes.apple.com/au/ar....tist/michael-sealey/

Subscribe for more hypnosis: http://www.youtube.com/subscri....ption_center?add_use

Popular Playlists:
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Hypnosis for Anxiety: https://www.youtube.com/playli....st?list=PLO9OtUmChpP

.........................

Deep trance hypnosis with guided relaxation for improving your subconscious mind power, boosting your natural memory recall ability, sharpening your study focus, and increasing your motivation for learning and information assimilation.

Take some time to improve your memory, study, focus, concentration, learning ability, or as you prepare for exams.

Improve your mind power and own abilities of self mind control to harness all of the resources of your natural intelligence.

Suitable for repeated listening as suggestions will compound over time. Good for relaxing before sleep, or for a brief mind's break or power nap during the day.

Do not listen to this recording whilst driving or operating machinery.

This recording is in no way a replacement for any prescribed medication; nor is it intended to contraindicate or supersede any medically diagnosed conditions. The author accepts no responsibility or liability for use of this material.

All hypnosis is self hypnosis. The power for positive change resides within your own mind. If you choose to accept the suggestions presented in this session, you can rest comfortably knowing that you are the one safely in control of your own positive results.

This channel uses positive voice suggestions, often accompanied with calm images, background sounds and meditation music, to allow you your very best state of relaxation, inner change, to remove negative blocks, and to create self guided healing therapy; with techniques from the fields of hypnotherapy, modern psychotherapy, trance work, guided relaxation, NLP, cognitive behavioural psychology, mindfulness meditation, and ASMR.

Peace & Enjoy

P.S. Please LINK & SHARE this video with those who you think might benefit from positive hypnosis and guided meditation. Thank you.

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Subconscious Confidence, Motivation, Self Esteem & Success: ► https://www.youtube.com/playli....st?list=PLO9OtUmChpP
Guided Self Healing & Positive Energy: ► https://www.youtube.com/playli....st?list=PLO9OtUmChpP
Lucid Dreaming Hypnosis: ► https://www.youtube.com/playli....st?list=PLO9OtUmChpP
Astral Travel & Astral Projection: ► https://www.youtube.com/playli....st?list=PLO9OtUmChpP
Hypnosis for Brain Power & Improved Self Mind Control: ► https://www.youtube.com/playli....st?list=PLO9OtUmChpP
Hypnosis with Binaural Beats & Brainwaves Music: ► https://www.youtube.com/watch?v=GI5yDfkv4a4
Hypnosis for Higher Self & Spirit Guide: ► https://www.youtube.com/playli....st?list=PLO9OtUmChpP
Hypnosis for ASMR: ► https://www.youtube.com/playli....st?list=PLO9OtUmChpP
_____
Original footage and photography by Michael Sealey

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Welcome to Intro to Deep Learning! This course is for anyone who wants to become a deep learning engineer. I'll take you from the very basics of deep learning to the bleeding edge over the course of 4 months. In this video, we’ll predict an animal’s body weight given it’s brain weight using linear regression via 10 lines of Python. I’ll have a live session every Wednesday at 10 AM PST that covers my weekly video topics in depth. You can click on the little notification bell next to the subscribe button to get an email notification whenever I’m live. And each session is recorded and uploaded to this channel in case you miss it. This Youtube content is 100% created by me (from the writing to the editing, etc.) , it’ll all be released on my channel, and it’s totally free.

I am also very proud and excited to announce my new, exclusive partnership with Udacity. Together, we’re offering the new Deep Learning Nanodegree Foundation program. If you want to take your game to the next level, this is for you! Especially since Udacity will be providing guaranteed admission to their groundbreaking Artificial Intelligence and Self-Driving Car Nanodegree programs to all graduates. They’re offering discounted limited-time pricing, so enroll now to enjoy the unique projects, program sets, and expert reviews. Plus, their community is amazing, so don’t forget to join the Slack channel after you enroll (I’ll be in there too!) And hey, I’m getting paid a small royalty from each enrollment, so let’s do this together!

Link to the Udacity nanodegree:
https://www.udacity.com/course..../deep-learning-nanod

Please Subscribe! And like. And comment. That’s what keeps me going.

This weeks coding challenge (these weekly challenges are not related to the Udacity nanodegree projects, those are additional):
https://github.com/llSourcell/....linear_regression_de

Mick’s winning code:
https://github.com/mickvanhulst/q_learning

Vishal’s runner-up code:
https://github.com/erilyth/Q-Learning-on-Mazes

More learning resources:
http://machinelearningmastery.....com/a-tour-of-machin
https://docs.microsoft.com/en-....us/azure/machine-lea
https://www.toptal.com/machine....-learning/machine-le
https://onlinecourses.science.....psu.edu/stat501/node
http://machinelearningmastery.....com/simple-linear-re

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

The vast majority of this course will use Tensorflow. It's just this first example that's using scikit-learn.

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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(** Python Data Science Training: https://www.edureka.co/python **)
In this video on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:

1. Introduction to Machine Learning

2. Types of Machine Learning

3. Supervised vs Unsupervised vs Reinforcement learning

4. Use Cases

Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE

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Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

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How it Works?

1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work

2. We have a 24x7 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 a Grade and a Verifiable Certificate!


- - - - - - - - - - - - - - - - -

About the Course

Edureka’s Data Science Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Data Science Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR.



During our Python Certification Training, our instructors will help you to:



1. Master the basic and advanced concepts of Python

2. Gain insight into the 'Roles' played by a Machine Learning Engineer

3. Automate data analysis using python

4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application

5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn

6. Explain Time Series and it’s related concepts

7. Perform Text Mining and Sentimental analysis

8. Gain expertise to handle business in the future, living the present

9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands-on Project Experience

- - - - - - - - - - - - - - - - - - -



Why learn Python?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.

Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.

Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.

For online Data Science training, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

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Daphne Koller is enticing top universities to put their most intriguing courses online for free -- not just as a service, but as a way to research how people learn. With Coursera (cofounded by Andrew Ng), each keystroke, quiz, peer-to-peer discussion and self-graded assignment builds an unprecedented pool of data on how knowledge is processed.

With Coursera, Daphne Koller and co-founder Andrew Ng are bringing courses from top colleges online, free, for anyone who wants to take them. Bio:
http://www.ted.com/speakers/daphne_koller.html

TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, global issues, the arts and more. Find closed captions and translated subtitles in a variety of languages at http://www.ted.com/translate.

Follow TED on Twitter: http://www.twitter.com/tednews
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If you have questions or comments about this or other TED videos, please go to http://support.ted.com

Home page: https://www.3blue1brown.com/
Brought to you by you: http://3b1b.co/nn2-thanks
And by Amplify Partners.

For any early stage ML startup founders, Amplify Partners would love to hear from you via 3blue1brown@amplifypartners.com

To learn more, I highly recommend the book by Michael Nielsen
http://neuralnetworksanddeeplearning.com/
The book walks through the code behind the example in these videos, which you can find here:
https://github.com/mnielsen/ne....ural-networks-and-de

MNIST database:
http://yann.lecun.com/exdb/mnist/

Also check out Chris Olah's blog:
http://colah.github.io/
His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great.

And if you like that, you'll *love* the publications at distill:
https://distill.pub/

For more videos, Welch Labs also has some great series on machine learning:
https://youtu.be/i8D90DkCLhI
https://youtu.be/bxe2T-V8XRs

"But I've already voraciously consumed Nielsen's, Olah's and Welch's works", I hear you say. Well well, look at you then. That being the case, I might recommend that you continue on with the book "Deep Learning" by Goodfellow, Bengio, and Courville.

Thanks to Lisha Li (@lishali88) for her contributions at the end, and for letting me pick her brain so much about the material. Here are the articles she referenced at the end:
https://arxiv.org/abs/1611.03530
https://arxiv.org/abs/1706.05394
https://arxiv.org/abs/1412.0233

Music by Vincent Rubinetti:
https://vincerubinetti.bandcam....p.com/album/the-musi

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3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that).

If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended

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What's actually happening to a neural network as it learns?
Next video: https://youtu.be/tIeHLnjs5U8
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Home page: https://www.3blue1brown.com/

The following video is sort of an appendix to this one. The main goal with the follow-on video is to show the connection between the visual walkthrough here, and the representation of these "nudges" in terms of partial derivatives that you will find when reading about backpropagation in other resources, like Michael Nielsen's book or Chis Olah's blog.

Watch us come OUT OF OUR COMFORT ZONES and learn how to dance from dancing professionals Maddie and Kenzie Ziegler. We must learn and master a choreographed dance routine in only 48 hours before performing it in front of qualified dance judges. INTENSITY WARNING!

Maddie's Channel - https://www.youtube.com/channe....l/UCTn6eUt2dRO_iaH_q
Maddie's Instagram - https://www.instagram.com/maddieziegler/?hl=en

Kenzie's Channel - https://www.youtube.com/channe....l/UC8AyTVmO6GWOrZHka
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Animation By - https://www.youtube.com/user/danababy1997/videos

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Last Week's Video - https://www.youtube.com/watch?v=r071nVMlTKw&t=1248s

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Ethan’s Stuff

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