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What is Machine Learning and how do businesses leverage it today? How does Machine Learning differ from Artificial Intelligence (AI) and Deep Learning, or are they all the same?
In this lightboard video, Luv Aggarwal with IBM Cloud, answers these questions and many more as he visually explains what Machine Learning is, how it compares to AI and Deep Learning, as well as why and how an enterprise would use a Machine Learning solution.

Chapters
0:00 – Intro
0:17 – Differences between Machine Learning, AI, and Deep Learning
1:32 – Supervised Learning
4:26 – Unsupervised Learning
6:38 – Reinforcement Learning
7:44 – Summary

#MachineLearning #AI #DeepLearning #watsonx

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40 thoughts on “What is Machine Learning?”
  1. i dont know if anyone will see this but i have a question, more like a guide path….im working on a project on fraud detection, please what are the steps i need to take. because im really confused

  2. This was a very good and succinct overview of terminology. I've been in technology R&D for 20 years but haven't interacted directly with AI/ML or other topics much. This is a very helpful primer and I'll be sure to binge-watch all of the other videos soon 🙂

  3. I spent the whole time wondering how he was writing on glass in front of him & the text was the right way around. Did he just write normally, & then they flopped (reversed) the image horizontally in edit?

  4. The biggest harm you do to people unfamiliar with these topics is telling them that algorithms or computers "learn". They never learn. They are soulless machines and algorithms. The computer executes the algorithm quickly and delivers the result – depending on the type of algorithm. If it is a "supervised" operation, we tell the computer what pattern to follow in the algorithm. So computers look for patterns in the data – either we show them these patterns, or they will be created according to the default assumptions of the algorithm. Computers will NEVER learn anything by themselves.

  5. What type of machine learning are Neural Networks? They have labelled data but we can also tell the machine whether it has reached a correct answer or not….so, are NNs a combination of RL and supervised learning?

  6. I guess you can't teach self-driving cars using RL in the real world (punishing them if they collide would be a little too late). So I would guess that RL is used only in lab simulations, and then later in the real world, these systems are only performing the task (without actively using RL). Do I get it right?

  7. Is there any tool/product in IBM which I can use to ingest various types of data who provides me built-in machine learning algo to predicts actionable insights based on pattern without spending anything for certain time or size of data? I really believe IBM is way advance in AI/ML than any other big players but its just how you market it.

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