Introduction to Artificial Intelligence

106,776 views

A2 Ciencia de Datos

Published on Premiered Aug 23, 2022
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✅ Check out our Online Artificial Intelligence Course Package: https://a2capacitacion.com/pack-intel... ???? Check out our Excel channel ????‍???? / a2capacitaci%c3%b3nexcel =============================================================== ❗Everything you need to know about Machine Learning in just 30 minutes❗ Machine Learning is “The science of programming computers to learn through different data sets” ???????? With it you can create systems that learn automatically. Can you imagine? ???????? In this video you will find the first chapter of our complete course with the necessary theory to introduce you to this world ???? and all the applications that can be made in real life, for example: ???? Spam filters ???? Robots that assemble the machines ???? Robot vacuum cleaner ???? The Instagram algorithm that makes you addicted to it We will also see topics such as: ???? Supervised Learning (Labeled Data) vs Unsupervised Learning (Data without labels) ???? Online Learning and Batch Learning ???? Semi-supervised Learning (Data set with only a few labels) ???? Reinforcement Learning (the sexiest and most complicated of all) ???? Instance-based or model-based learning And many more topics! ???? that you will surely love ???? ❗What are you waiting to discover all the scope of this Revolutionary Science❗ ???? ================================================================= CHECK OUT THE WORST EXCEL MEMES ON OUR NETWORKS! ???????? ► Instagram: / a2excel ► Facebook: / a2capacitacion ► TikTok: / a2excel ================================================================= 00:00 Intro that is not an Intro 00:23 Getting to know Machine Learning 01:37 Definition of Machine Learning 03:11 Why use Machine Learning instead of traditional programming? 05:46 Examples of its applications 06:32 Supervised Learning vs Unsupervised Learning 11:07 Semi-supervised Learning 11:59 Reinforcement Learning 14:24 Online Learning and Batch Learning 17:17 Instance-based or model-based learning 21:10 Mini summary 21:23 What could go wrong? 27:09 How to know if your algorithm is working or not? 28:43 Hyperparameters

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