"️🔥 Michigan Engineering - Professional Certificate in AI and Machine Learning - https://www.simplilearn.com/professional-aiml-program?utm_campaign=ukzFI9rgwfU&utm_medium=DescriptionFFF&utm_source=Youtube ️🔥IITK - Professional Certificate Course in Generative AI and Machine Learning - https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=ukzFI9rgwfU&utm_medium=DescriptionFFF&utm_source=Youtube ️🔥Microsoft AI Engineer Program - https://www.simplilearn.com/ai-engineer-course?utm_campaign=ukzFI9rgwfU&utm_medium=DescriptionFFF&utm_source=Youtube ️️🔥 Applied Generative AI Specialization - https://www.simplilearn.com/applied-ai-course?utm_campaign=ukzFI9rgwfU&utm_medium=DescriptionFFF&utm_source=Youtube" This Machine Learning basics video will help you understand what Machine Learning is, what are the types of Machine Learning - supervised, unsupervised & reinforcement learning, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into self-learning mode without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results. The below topics are explained in this Machine Learning basics video: 1. What is Machine Learning? ( 00:21 ) 2. Types of Machine Learning ( 02:43 ) 2. What is Supervised Learning? ( 02:53 ) 3. What is Unsupervised Learning? ( 03:46 ) 4. What is Reinforcement Learning? ( 04:37 ) 5. Machine Learning applications ( 06:25 ) Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Download the Machine Learning Career Guide to explore and step into the exciting world of Machine Learning and follow the path toward your dream career- https://bit.ly/3eLuTUo Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearning #WhatIsMachineLearning #MachineLearningTutorial #MachineLearningBasics #MachineLearningTutorialForBeginners #Simplilearn ➡️ About Artificial Intelligence Engineer This Artificial Intelligence Engineer course Created in partnership with IBM, this course introduces students to blended learning and prepares them to be AI and Data Science specialists. In Armonk, New York, IBM is a significant cognitive service and integrated cloud solution firm that provides many technology and consulting solutions. IBM is a leader in AI and Machine Learning technology verticals for 2021. This AI masters course will prepare students for Artificial Intelligence and Data Analytics careers. ✅ Key Features - Add the IBM Advantage to your Learning - 25 Industry-relevant Projects and Integrated labs - Immersive Learning Experience - Simplilearn's JobAssist helps you get noticed by top hiring companies ✅ Tools Covered - ChatGPT - Flask - Matplotlib - django - Python - Numpy - Pandas - SciPy - Keras - OpenCV - And Many More… 👉Learn More at: https://www.simplilearn.com/professional-aiml-program?utm_campaign=MachineLearningscribe&utm_medium=DescriptionFirstFold&utm_source=youtube
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**Summary**: - machine learning is the general term for when computers learn from data - there are lots of different ways ("algorithms") that machines can learn - the algorithms can be grouped into supervised, unsupervised, and reinforcement algorithms* - the data that you feed to a machine learning algorithm can be input-output pairs or just inputs - supervised learning algorithms require input-output pairs (i.e. they require the output) - unsupervised learning requires only the input data (not the outputs) - here is how, in general, supervised algorithms work: - you feed it an example input, then the associated output - you repeat the above step many many times - eventually, the algorithm picks up a pattern between the inputs and outputs - now, you can feed it a brand new input, and it will predict the output for you - here is how, in general, unsupervised algorithms work: - you feed it an example input (without the associated output) - you repeat the above step many times - eventually, the algorithm clusters your inputs into groups - now, you can feed it a brand new input, and the algorithm will predict which cluster it belongs with * the first example in this video used the k-nearest neighbor algorithm, which is a supervised machine learning algorithm Hope that was useful to someone! Thanks for the video, really enjoyed it!! :)
Literally learnt more from you than 4 years in college
"Hey Siri, can you remind me to book a cab at 6 pm today?" "Here's what i found on the web for Keanu Reeves' Sixteenth Birthday" 😐
I'm impressed by the way you taught. Teacher should to be like you.
Quite great. An Amazing one explaining the ML basis.!! 1. Supervised learning. 2. Supervised learning after Feedback (Rein inforced learning) 3. Unsupervised learning.
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Labeled =supervised Unlabeled= Un-supervised And finally Enforcement Learning = Learning from results and upgrading . Tq for the explanation
I am from a health care background, but I could effortlessly understand everything she said. Excellent introduction.
wow! this is my first time actually researching this topic being a computer science student. i have got to say, this really brightened my mood and brought some light to my day/mind regarding my major! :) awesome stuff!
In YouTube, It can display the videos as per our frequent past search.
youtube recommended videos are the biggest example of machine learning , bcoz it recommends us videos on the basis of our history. AM I CORRECT?
wonderful and fantastic tutorial! It's really helpful. The explanation is so clear. thumb up to the tutor.
Well explained by this video :) Scenario 1: Supervised Learning. Scenario 2: Supervised Learning. Scenario 3: Unsupervised Learning.
Machine learning is a game changer 📈
The video was quite interesting and informative. I would like to be your part of learning ML.
1) Facebook photo recognition based on tags in an example of supervised learning 2) NetFlix Movie recommendation is an example of unsupervised learning 3) Bank Fraud Detection is an example of reinforcement learning
Respected ma'am, the video was highly informative. Thank you ma'am for teaching so many concepts about machines😄😄
yeah wow!!! you explained so nice...😍😍 ans is 1. super 2. super 3.unsuper am i correct???
Loved the video..it's very informative and insightful under 8 mins.. Quiz Answers: 1st and 2nd are supervised while 3rd is unsupervised
This video is quiet frankly down to point. I was even excited when I begun this field and the different things you could indulge in and improve for a business. It really is helping me and my career. I am even starting my own channel to breakdown some of the concepts that I found hard to understand about different algorithms and how they work. Check it out and for any starters, do tell me what you find hard at first to grasp when begging into the field ☺️