Machine Learning can be defined as :
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E
Deep Learning can be defined as:
Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.
From the above definitions, it can be inferred that Deep Learning is a sub-branch of Machine Learning. Also, deep learning alogos are used to provide a greater understanding of data and also to get a more accurate model, i.e. Deep Learning comes into picture when the traditional Machine Learning alogos fail to provide the desired results.
Deep Learning is used in scenarios where high-end calculations are needed i.e. the amount of data is huge for the traditional ML alogos to handle and process.
Machine Learning finds its usage in providing the base for deep learning to work
ML Algorithms
DL Algorithms
Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.Machine learning and deep learning, actually deep learning and machine learning both are the subsets of AIMachine Learning:Data mining is a technique of examining a large pre-existing database and extracting new information from that database, it’s easy to understand, right, machine learning does the same, in fact, machine learning is a type of data mining technique.Deep Learning: Deep learning is actually a subset of machine learning. It technically is machine learning and functions in the same way but it has different capabilities.The main difference between deep and machine learning is, machine learning models become better progressively but the model still needs some guidance. If a machine learning model returns an inaccurate prediction then the programmer needs to fix that problem explicitly but in the case of deep learning, the model does it by himself. Automatic car driving system is a good example of deep learning.Let’s take an example to understand both machine learning and deep learning – Suppose we have a flashlight and we teach a machine learning model that whenever someone says “dark” the flashlight should be on, now the machine learning model will analyse different phrases said by people and it will search for the word “dark” and as the word comes the flashlight will be on but what if someone said “I am not able to see anything the light is very dim”, here the user wants the flashlight to be on but the sentence does not the consist the word “dark” so the flashlight will not be on. That’s where deep learning is different from machine learning. If it were a deep learning model it would on the flashlight, a deep learning model is able to learn from its own method of computing.