So, AI is the all-encompassing concept that initially erupted, then followed by ML that thrived later, and lastly DL that is promising to escalate the advances of AI to another level.
Let’s dig deeper so that you can understand which is better for your specific use case: artificial intelligence, machine learning, or deep learning.
What is artificial intelligence?
Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions.
As the name suggests, artificial intelligence can be loosely interpreted to mean incorporating human intelligence to machines.
Artificial intelligence is the broader concept that consists of everything from Good Old-Fashioned AI (GOFAI) all the way to futuristic technologies such as deep learning.
Whenever a machine completes tasks based on a set of stipulated rules that solve problems (algorithms), such an “intelligent” behavior is what is called artificial intelligence.
For example, such machines can move and manipulate objects, recognize whether someone has raised the hands, or solve other problems.
AI-powered machines are usually classified into two groups — general and narrow. The general artificial intelligence AI machines can intelligently solve problems, like the ones mentioned above.
The narrow intelligence AI machines can perform specific tasks very well, sometimes better than humans — though they are limited in scope.
The technology used for classifying images on Pinterest is an example of narrow AI.
What is machine learning?
Machine learning is based on algorithms that can learn from data without relying on rules-based programming.
As the name suggests, machine learning can be loosely interpreted to mean empowering computer systems with the ability to “learn”.
The intention of ML is to enable machines to learn by themselves using the provided data and make accurate predictions.
ML is a subset of artificial intelligence; in fact, it’s simply a technique for realizing AI.
It is a method of training algorithms such that they can learn how to make decisions.
Training in machine learning entails giving a lot of data to the algorithm and allowing it to learn more about the processed information.
For example, here is a table that identifies the type of fruit based on its characteristics:
As you can see on the table above, the fruits are differentiated based on their weight and texture.
However, the last row gives only the weight and texture, without the type of fruit.
And, a machine learning algorithm can be developed to try to identify whether the fruit is an orange or an apple.
After the algorithm is fed with the training data, it will learn the differing characteristics between an orange and an apple.
Therefore, if provided with data of weight and texture, it can predict accurately the type of fruit with those characteristics.
What is deep learning?
As earlier mentioned, deep learning is a subset of ML; in fact, it’s simply a technique for realizing machine learning. In other words, DL is the next evolution of machine learning.
DL algorithms are roughly inspired by the information processing patterns found in the human brain.
Just like we use our brains to identify patterns and classify various types of information, deep learning algorithms can be taught to accomplish the same tasks for machines.
The brain usually tries to decipher理解，解释，辨认 the information it receives. It achieves this through labelling and assigning the items into various categories.
Whenever we receive a new information, the brain tries to compare it to a known item before making sense of it — which is the same concept deep learning algorithms employ采用.
For example, artificial neural networks (ANNs) are a type of algorithms that aim to imitate模仿 the way our brains make decisions.
Comparing deep learning vs machine learning can assist you to understand their subtle细微的 differences.
For example, while DL can automatically discover the features to be used for classification, ML requires these features to be provided manually.
Furthermore, in contrast to ML, DL needs high-end machines and considerably big amounts of training data to deliver accurate results.
Difference between Supervised and Unsupervised Learning
Supervised有监督的 learning: Supervised learning is the learning of the model where with input variable ( say, x) and an output variable (say, Y) and an algorithm to map the input to the output.
That is, Y = f(X)
Why supervised learning?
The basic aim is to approximate the mapping function(mentioned above) so
well that when there is a new input data (x) then the corresponding
output variable can be predicted.
It is called supervised learning because the process of an learning(from the training dataset) can be thought of as a teacher who is supervising the entire learning process.
Thus, the “learning algorithm” iteratively makes predictions on the training data and is corrected by the “teacher”, and the learning stops when the algorithm achieves an acceptable level of performance(or the desired accuracy).
Example of Supervised Learning
Suppose there is a basket which is filled with some fresh fruits, the task is to arrange the same type of fruits at one place.
Also, suppose that the fruits are apple, banana, cherry, grape.
Suppose one already knows from their previous work (or experience) that, the shape of each and every fruit present in the basket so, it is easy for them to arrange the same type of fruits in one place.
Here, the previous work is called as training data in Data Mining terminology. So, it learns the things from the training data. This is because it has a response variable which says y that if some fruit has so and so features then it is grape, and similarly for each and every fruit.
This type of information is deciphered from the data that is used to train the model.
This type of learning is called Supervised Learning.
Such problems are listed under classical Classification Tasks.
Unsupervised无监督的 Learning: Unsupervised learning is where only the input data (say, X) is present and no corresponding output variable is there.
Why Unsupervised Learning?
The main aim of Unsupervised learning is to model the distribution in the data in order to learn more about the data.
It is called so, because there is no correct answer and there is no such teacher(unlike supervised learning). Algorithms are left to their own devises to discover and present the interesting structure in the data.
Example of Unsupervised Learning
Again, Suppose there is a basket and it is filled with some fresh
fruits. The task is to arrange the same type of fruits at one place.
This time there is no information about those fruits beforehand, its the first time that the fruits are being seen or discovered
So how to group similar fruits without any prior knowledge about those.
First, any physical characteristic of a particular fruit is selected. Suppose color.
Then the fruits are arranged on the basis of the color. The groups will be something as shown below:
RED COLOR GROUP: apples & cherry fruits.
GREEN COLOR GROUP: bananas & grapes.
So now, take another physical character say, size, so now the groups will be something like this.
RED COLOR AND BIG SIZE: apple.
RED COLOR AND SMALL SIZE: cherry fruits.
GREEN COLOR AND BIG SIZE: bananas.
GREEN COLOR AND SMALL SIZE: grapes.
The job is done!
Here, there is no need to know or learn anything beforehand. That means,
no train data and no response variable. This type of learning is known
as Unsupervised Learning.
Difference b/w Supervised and Unsupervised Learning :
| SUPERVISED LEARNING | UNSUPERVISED LEARNING | |
|---|---|---|
| Input Data | Uses Known and Labeled Data as input | Uses Unknown Data as input |
| Computational Complexity | Very Complex | Less Computational Complexity |
| Real Time | Uses off-line analysis | Uses Real Time Analysis of Data |
| Number of Classes | Number of Classes are known | Number of Classes are not known |
| Accuracy of Results | Accurate and Reliable Results | Moderate Accurate and Reliable Results |
Tools
Rstudio
RStudio’s recommended professional data science solution for every team.
Python
Databricks
is an industry-leading, cloud-based data engineering tool used for processing and transforming massive quantities of data and exploring the data through machine learning models.
Recently added to Azure, it's the latest big data tool for the Microsoft cloud.
Jupyter
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text.
Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.
Power BI
Power BI is Microsoft's interactive data visualization and analytics tool for business intelligence (BI).
You can use it to pull data from a wide range of systems in the cloud and on premises and create dashboards that track the metrics you care about the most, or drill in and (literally) ask questions about your data.
https://docs.microsoft.com/en-us/power-bi/fundamentals/