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AI vs Machine Learning vs Deep Learning: What's the Difference?

Artificial intelligence is imparting a cognitive ability to a machine.

In this tutorial, you will learn- Machine learning is the best tool so far to analyze, understand and identify a pattern in the data.

One of the main ideas behind machine learning is that the computer can be trained to automate tasks that would be exhaustive or impossible for a human being.

The clear breach from the traditional analysis is that machine learning can take decisions with minimal human intervention.

Machine learning uses data to feed an algorithm that can understand the relationship between the input and the output.

When the machine finished learning, it can predict the value or the class of new data point.

It is a subset of machine learning and is called deep learning because it makes use of deep neural networks.

In the example, the classifier will be trained to detect if the image is a: The four objects above are the class the classifier has to recognize.

Training an algorithm requires to follow a few standard steps: The first step is necessary, choosing the right data will make the algorithm success or a failure.

The training set would be fed to a neural network Each input goes into a neuron and is multiplied by a weight.

it provides an actual value for the regression task and a probability of each class for the classification task.

The neural network is fully trained when the value of the weights gives an output close to the reality.

For instance, a well-trained neural network can recognize the object on a picture with higher accuracy than the traditional neural net.

A crucial part of machine learning is to find a relevant set of features to make the system learns something.

For example, an image processing, the practitioner needs to extract the feature manually in the image like the eyes, the nose, lips and so on.

The network applies a filter to the picture to see if there is a match, i.e., the shape of the feature is identical to a part of the image.

When there is enough data to train on, deep learning achieves impressive results, especially for image recognition and text translation.

Data Science vs. Deep Learning vs. Machine Learning vs. Artificial Intelligence

In the modern world, using data as the fuel, Data Science drives different technologies toward automation.

Watch Data Science vs Artificial Intelligence Tutorial For analyzing and visualizing a huge amount of data, we use various statistical methods.

When a user visits the website, Flipkart’s recommendation system marks and records the behavior and the movements of the visitor.

Also, whenever the user clicks on a product, the system stores it, along with other user activities, in the database.

Then, it uses collaborative filtering for suggesting products to customers who are likely to churn out for specific products.

Further, it also gathers and uses the data of ratings provided by buyers for a particular product.

Then, the recommendation engine combines and analyzes all this data with the help of tools available in Data Science.

It uses statistical analysis for visualizing and understanding the behavior of data in a neat and clean manner.

By this example, we can infer how crucial the data is and how the tools of Data Science pave way for the growth of any business.

Artificial Intelligence is the imposition of humans intelligence to robots or machines, especially computer systems.

It involves a three-step process: Artificial Intelligence consists of various frameworks that can be employed for computations in deep neural networks.

Strong Artificial Intelligence: In strong AI, the algorithms and instructions for a machine are designed such that they give the machine the ability to learn by itself from the given inputs and iteratively enhance accuracy by experience.

these algorithms inject abilities into a machine for performing distinct tasks without being denotatively programmed.

Before diving into the types of Machine Learning, if we talk about Machine Learning vs statistics, we would agree with the fact that visualization is not possible without performing accurate statistical analyses.

With the help of statistics, we can draw useful insights from data and build effective Machine Learning models on top of it.

Example: Weather forecasting is done on the basis of some labeled factors such as humidity, wind, temperature, atmospheric pressure, and precipitation.

It altogether gives a numeric value that predicts the weather condition, based on the above-mentioned factors.

Every time the player gets stuck in a dead-end, −10 points (punishment) are given.

Finally, after receiving many punishments and rewards, the player finds the right path to escape.

The layered architecture (deep neural network) in Deep Learning is inspired by the human biological neural network.

The outcome of this augmentation continues to move to the subsequent layer and acts as the input for that layer.

After that, at the output layer, the neural network yields an actual value for the regression task.

Finally, when the corresponding values of the weights give the output near to the actual, the neural network is fully trained.

Interestingly, a fully-trained neural network is capable of identifying an entity with greater efficiency in comparison with the regular neural network.

When we type a certain text or a word in Google, it recommends the relevant searches according to the keyword.

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