Machine Learning
Machine learning refers to any type of computer program that can “learn” by itself without having to be explicitly programmed by a human.
The phrase (and its underlying idea) has its origins decades ago – all the way to Alan Turing’s seminal 1950 paper “Computing Machinery and Intelligence,” [1], which featured a section on his famous “Learning Machine” that could fool a human into believing that it’s real.
Data scientists are expected to be familiar with the differences between supervised machine learning and unsupervised machine learning
Supervised learning
In supervised learning, the user trains the program to generate an answer based on a known and labeled data set. Classification and regression algorithms, including random forests, decision trees, and support vector machines, are commonly used for supervised learning tasks.
Unsupervised machine learning
In unsupervised machine learning, the algorithms generate answers on unknown and unlabeled data. Data scientists commonly use unsupervised techniques for discovering patterns in new data sets. Clustering algorithms, such as K-means, are often used in unsupervised machine learning.
Deep Learning
Deep learning is a form of machine learning that can utilize either supervised or unsupervised algorithms, or both.
Deep learning is a form of machine learning that can utilize either supervised or unsupervised algorithms, or both. While it’s not necessarily new, deep learning has recently seen a surge in popularity as a way to accelerate the solution of certain types of difficult computer problems, most notably in the computer vision and natural language processing (NLP) fields.
Here are some differences between Machine Learning and Deep Learning:
- The main difference between deep learning and machine learning is due to the way data is presented in the system. Machine learning algorithms almost always require structured data, while deep learning networks rely on layers of ANN (artificial neural networks).
- Machine learning algorithms are designed to “learn” to act by understanding labelled data and then use it to produce new results with more datasets. However, when the result is incorrect, there is a need to “teach them” >>>>> more training.
- Deep learning networks do not require human intervention, as multilevel layers in neural networks place data in a hierarchy of different concepts, which ultimately learn from their own mistakes. However, even they can be wrong if the data quality is not good enough.
- Data decides everything. It is the quality of the data that ultimately determines the quality of the result.
Moreover, in the links below, there are some works that used and reviewed both of these techniques:
- Article From machine learning to deep learning: Progress in machine ...
- Article Machine Learning and Deep Learning frameworks and libraries ...
- Article Machine Learning and Deep Learning Methods for Intrusion Det...
- Conference Paper Classifying Multilingual User Feedback using Traditional Mac...
I hope it will be useful for you
Thank you
To me, Deep learning is a subset of machine learning and machine learning is a subset of artificial intelligence. Some other people may have different view, read for example in https://hackernoon.com/deep-learning-vs-machine-learning-a-simple-explanation-47405b3eef08
Machine Learning in the most basic way is the practice of using algorithms to collect data, learn from it, and then decide or prediction about something in the world. So instead of implementing the software routines at hand, with a specific set of instructions to complete a particular task, the machine is “trained” using a large amount of data and algorithms that give it the ability to learn how to execute the assignment.
Deep Learning is a technique to Implement Machine Learning.
Basically, the main difference lies in the features extraction task, which is manual in the Machine Learning (ML) and automatic in the Deep Learning (DL is a subset of ML) (please see the fig. https://tinyurl.com/vhq9mv6). This is why the DL (programmable artificial neural network) requires a much larger amounts of data than the ML. Indeed, DL is an "evolution" of ML, but ML techniques or IF/ELSE algorithms can be more efficient than DL according to the data sets used and the goal of the project.
Caveat: The definitions below have been deliberately subjected to verbiage.
Machine Learning: The task where a machine discovers patterns from a given data set called 'training data' and then leverages the patterns, discovered, to forecast beneficial knowledge based on new data called 'test data'.
Deep Learning: Discovering patterns, as stated above can be done either
- involving human intervention: Feature Engineering, or
- let the machine do it for you: Representation Learning
Deep Learning is an approach of representation learning where the patterns are discovered(built, to be precise) hierarchically in a bottom-up fashion: beginning with the atomic representations(which are indivisible) and gradually building up to more sophisticated, abstract representations((which are divisible into atomic representations).
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Now since Deep Learning involves discovering patterns, which is a subtask of Machine Learning, as stated above, it is a subdomain of Machine Learning.
In simple we can say that
Machine Learning is manual, however, deep learning automatically extract the features, Without the use of manual hand coded rules.
Machine Learning is the umbrella that includes many techniques including the deep learning as a subset of the machine learning.
There are many differences between machine learning and deep learning. In what perspective are you looking for?
In my opinion, a higher level of difference is associated with feature extraction and is automatic in deep learning. Whereas, machine learning requires the intervention of researchers.
Regards
The main difference is that machine learning approaches required manual feature extraction then these features are used to train the classifier. They are also sensitive to different sizes and shapes.
However,
Deep learning models do the feature extraction and classification in one shot in automatic way. One of the cons of deep learning is required large amount of data for training.
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