Machine Learning vs Deep Learning vs Artificial Intelligence | ML vs DL vs AI | Simplilearn

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This Machine Learning vs Deep Learning vs Artificial Intelligence video will help you understand the differences between ML, DL and AI, and how they are related to each other. The tutorial video will also cover what Machine Learning, Deep Learning and Artificial Intelligence entail, how they work with the help of examples, and whether they really are all that different.

This Machine Learning Vs Deep Learning Vs Artificial Intelligence video will explain the topics listed below:

1. Artificial Intelligence example ( 00:29 )
2. Machine Learning example ( 01:29 )
3. Deep Learning example ( 01:44 )
4. Human vs Artificial Intelligence ( 03:34 )
5. How Machine Learning works ( 06:11 )
6. How Deep Learning works ( 07:09 )
7. AI vs Machine Learning vs Deep Learning ( 12:33 )
8. AI with Machine Learning and Deep Learning ( 13:05 )
9. Real-life examples ( 15:29 )
10. Types of Artificial Intelligence ( 17:50 )
11. Types of Machine Learning ( 20:32 )
12. Comparing Machine Learning and Deep Learning ( 22:46 )
13. A glimpse into the future ( 25:46 )

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You can also go through the Slides here: https://goo.gl/cdQ7uy

#SimplilearnMachineLearning #SimplilearnAI #SimplilearnDeepLearning #Artificialintelligence #MachineLearningTutorial

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About Simplilearn Artificial Intelligence Engineer course:

What are the learning objectives of this Artificial Intelligence Course?

By the end of this Artificial Intelligence Course, you will be able to accomplish the following:
1. Design intelligent agents to solve real-world problems which are search, games, machine learning, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, agent decision making
2. Master TensorFlow by understanding the concepts of TensorFlow, the main functions, operations and the execution pipeline
3. Acquire a deep intuition of Machine Learning models by mastering the mathematical and heuristic aspects of Machine Learning
4. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
5. Comprehend and correlate between theoretical concepts and practical aspects of Machine Learning
6. Master and comprehend advanced topics like convolutional neural networks, recurrent neural networks, training deep networks, high-level interfaces

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What skills will you learn with our Masters in Artificial Intelligence Program?

1. Learn about major applications of Artificial Intelligence across various use cases in various fields like customer service, financial services, healthcare, etc
2. Implement classical Artificial Intelligence techniques such as search algorithms, neural networks, tracking
3. Ability to apply Artificial Intelligence techniques for problem-solving and explain the limitations of current Artificial Intelligence techniques
4. Formalise a given problem in the language/framework of different AI methods such as a search problem, as a constraint satisfaction problem, as a planning problem, etc

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Comments

Lemonade says:

So is life just a super computer that computes to make the best actions to survive and reproduce? Is that all we are? Just programmed to survive?

Srinivas Guttikonda says:

Nice presentation!

Akshat says:

Awesome presentation. Keep up the good work👍

PremiumShiny says:

17:40 i just realised that Sophia the robot was there when he mentioned chat bots lol. writing an essay on deep learning and emotion

girish lc says:

Is DL is an Unsupervised learning ?

NAMAN DUBEY says:

Your certifications course or too expensive as a student how we can effort it.

Thaís Sena says:

Very good!!

אבישי ב says:

Great video! How can I download the slides? Thanks a lot!

turbodave100 says:

Very informative. Thank you!

Erick Kasiulis says:

Hire an editor –
Please correct the slide at 4:06 (Should be make not take)

Happy Dog says:

What is the best language to program it?

templargfx says:

Has anyone tried copying the way conciousness functions into an AI program?

Bhagya Anant says:

How data science is useful in electronics engineering field? How can I use my EXTC engg knowledge in ML or AI or AR or VR ?

Pritish Shardul says:

@07:12: It says that the Deep Learning model works with a large unlabeled data. But to train the model, i.e. to tell it whether the Bicycle Image was correctly classified as Bicycle or not, we need the input image labelled as Bicycle in the first place, right? (Back propagation).
Doesn't that mean we need a large labeled data for deep learning..?

Gokul Chowathu says:

Thank You!! Very well explained. Have to explore further into this

Deepanshu Rawat says:

Very Well Explained

Raymond says:

the 3 types of ML: 21:07

Great video, great slides

Zachary Mitchell says:

Cool, and yes, been waiting for this video

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