week-1
The Terminology of AI https://www.coursera.org/learn/ai-for-everyone/lecture/Q1EJD/the-terminology-of-ai
Hello, I don’t understand the difference between deep learning and machine learning? So, as far as I understand, machine learning is about teaching the algorithm using input-output mapping for it then to be able to answer any related question on its own; is that correct? If so, how is it different from deep learning?
Machine Learning is a more general term that covers essentially all forms of ML, including supervised and unsupervised algorithms and things like Reinforcement Learning, whereas Deep Learning is a more specific type of supervised learning algorithm that uses deep neural networks.
I found this diagram by googling “Venn diagram machine learning”. 
Think of machine learning as teaching computers to learn from examples. It’s like showing a computer lots of pictures of cats and dogs and telling it, “This is a cat, and that’s a dog.” The computer learns to recognize patterns and make predictions based on what it has seen.
Now, deep learning is a cool technique within machine learning. It’s like giving the computer a more sophisticated brain, inspired by how our brains work. This brain, called a neural network, has many layers that work together. Each layer helps the computer understand different features, like fur color or ear shape.
I would say machine learning is a family that incorporates algorithms used in deep learning(ANN) and data science.
From what I have understood. Please correct me here:
Any form of machine learning delivers A->B mapping. Dataset relationship remains one to one
Deep learning is a special subset of machine learning.
Neural network algorithm in DL looks many to many relationships in dataset A to arrive at B.