Machine Learning is one of those things that is chock full of hype and confusion terminology. In this StatQuest, we cut through all of that to get at the most basic ideas that make a foundation for the whole thing. These ideas are simple and easy to understand. After watching this StatQuest, you'll be ready to learn all kinds of new and exciting things about Machine Learning.

If you're interested in learning more about SoSA, here's the link: http://thesosa.org/

Here's the link to the video about the bias/variance tradeoff:
https://youtu.be/EuBBz3bI-aA

Here's the link to the video about cross-validation, aka the way to determine which samples go into your training set and which samples go into your testing set: https://youtu.be/fSytzGwwBVw

For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/

If you'd like to support StatQuest, please consider...

Buying The StatQuest Illustrated Guide to Machine Learning!!!
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC

Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channe....l/UCtYLUTtgS3k1Fg4y5

...a cool StatQuest t-shirt or sweatshirt:
https://shop.spreadshirt.com/s....tatquest-with-josh-s

...buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/

...or just donating to StatQuest!
https://www.paypal.me/statquest

Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
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0:00 Awesome song and introduction
0:35 A silly example of classification
2:24 A silly example of regression
3:37 The Bias/Variance Tradeoff
8:15 Fancy machine learning
8:56 Evaluating the performances of a decision tree
11:12 Summary of concepts and main ideas


#statquest #ML

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This is my solution to predict.m function in Programming assignment 3 from the famous Machine Learning course by Andrew Ng.

Github: https://github.com/AladdinPerz....on/Courses/tree/mast

With so many alternatives available, why are neural nets used for Deep Learning? Neural nets excel at complex pattern recognition and they can be trained quickly with GPUs.

Deep Learning TV on
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Twitter: https://twitter.com/deeplearningtv

Historically, computers have only been useful for tasks that we can explain with a detailed list of instructions. As such, they tend to fail in applications where the task at hand is fuzzy, such as recognizing patterns. Neural Networks fill this gap in our computational abilities by advancing machine perception – that is, they allow computers to start to making complex judgements about environmental inputs. Most of the recent hype in the field of AI has been due to progress in the application of deep neural networks.

Neural nets tend to be too computationally expensive for data with simple patterns; in such cases you should use a model like Logistic Regression or an SVM. As the pattern complexity increases, neural nets start to outperform other machine learning methods. At the highest levels of pattern complexity – high-resolution images for example – neural nets with a small number of layers will require a number of nodes that grows exponentially with the number of unique patterns. Even then, the net would likely take excessive time to train, or simply would fail to produce accurate results.

Have you ever had this problem in your own machine learning projects? Please comment.

As a result, deep nets are essentially the only practical choice for highly complex patterns such as the human face. The reason is that different parts of the net can detect simpler patterns and then combine them together to detect a more complex pattern. For example, a convolutional net can detect simple features like edges, which can be combined to form facial features like the nose and eyes, which are then combined to form a face (Credit: Andrew Ng). Deep nets can do this accurately – in fact, a deep net from Google beat a human for the first time at pattern recognition.

However, the strength of deep nets is coupled with an important cost – computational power. The resources required to effectively train a deep net were prohibitive in the early years of neural networks. However, thanks to advances in high-performance GPUs of the last decade, this is no longer an issue. Complex nets that once would have taken months to train, now only take days.

Credits:
Nickey Pickorita (YouTube art)
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

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Thanks to Brilliant for sponsoring this video :-)

Grokking Deep Learning is a great introduction to Deep Learning.

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Hi data nerds! Today I’m sharing with you my recommended learning roadmap and the courses to help you learn the core data science skills quickly. Resources are abundant online, but it is hard sometimes to select what is right for you, and what you really need. I hope with this learning roadmap, you can learn the most in-demand skills in the most effective way. Hope you enjoy it!

Thank you for watching and have a great day! 🤗

🔑 TIMESTAMPS
================================
0:00 - Intro
0:17 - My data science journey 🤓
2:12 - Google Data Analytics Professional cert
4:14 - Learn SQL Basic for DS Specialization
6:37 - Applied Data Science with Python Specialization
8:34 - Mathematics for Machine Learning Specialisation
10:50 - Data Visualisation with Tableau Specialisation
13:45 - Tips for saving $$


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