Understanding Artificial Intelligence, Machine Learning and Deep Learning

On 23 Jun., 2022

Pre-processing, analysis, visualization, and prediction are all part of the Data Science process.

Understanding Artificial Intelligence, Machine Learning and Deep Learning

Artificial Intelligence (AI) and its subsets Machine Learning (ML) and Deep Learning (DL) are becoming increasingly important in Data Science.
Pre-processing, analysis, visualisation, and prediction are all part of the Data Science process.
Let's take a deep dive into artificial intelligence and its subsets
Artificial intelligence (AI) is a subfield of computer science concerned with creating intelligent machines capable of doing activities that normally require human intelligence.
AI is broadly classified into three groups, as shown below.

Narrow Artificial Intelligence (ANI)

Artificial Super Intelligence (ASI) Artificial General Intelligence (AGI) (ASI).

Narrow AI, often known as 'Weak AI,' excels at performing a single task in a specific manner.
For example, an automated coffee machine robs that follows a predefined set of steps to make coffee.
AGI, sometimes known as 'Strong AI,' performs a wide range of tasks that require thinking and reasoning like a human.
Examples include Google Assist, Alexa, and chatbots that use Natural Language Processing (NPL).
The upgraded version, Artificial Super Intelligence (ASI), outperforms human skills.
It is capable of engaging in creative activities such as painting, decision making, and emotional connections.

Let us now look into Machine Learning (ML).
It is a subset of AI that involves the modelling of algorithms to aid in the prediction of complex data patterns and sets.
Machine learning is concerned with enabling algorithms to learn from provided data, collect insights, and make predictions about previously unanalyzed data using the information gathered.
There are various machine learning methods.

supervised education (Weak AI - Task driven)

semi-supervised learning non-supervised learning (Strong AI - Data Driven) (Strong AI -cost effective)

machine learning was reinforced
(Powerful AI that learns from mistakes)

Supervised machine learning makes use of historical data to analyse behaviour and make predictions for the future.
In this case, the system is made up of a single dataset.
It has parameters for the input and output labelled.
And as fresh data arrives, the ML algorithm analyses it and provides the exact output based on the fixed parameters.
Supervised learning is capable of performing classification and regression tasks.
Classification tasks include picture classification, face recognition, email spam classification, identify fraud detection, and so on, whereas regression tasks include weather forecasting, population growth prediction, and so on.

Unsupervised Machine Learning Institute in Delhi

makes no use of labelled or categorised parameters.
It focuses on detecting latent structures in unlabeled data to assist systems in correctly inferring a function.
They employ techniques like clustering and dimensionality reduction.
Clustering is the process of grouping data points that have similar metrics.
It is data-driven, and some instances of clustering include Netflix movie recommendations, consumer segmentation, purchasing habits, and so on.
Examples of dimensionality reduction include feature elicitation and large data visualisation.

Semi-supervised machine learning improves learning accuracy by using both labelled and unlabeled data.
When labelling data proves to be costly, semi-supervised learning can be a cost-effective approach.

Reinforcement learning differs from both supervised and unsupervised learning.
It can be characterised as a process of trial and error that eventually yields results.
It is accomplished through the iterative improvement cycle principle (to learn by past mistakes).
Reinforcement learning has also been used to train agents to drive autonomously in simulated scenarios.
Reinforcement learning algorithms include Q-learning.

Moving on, Deep Learning (DL) is a subset of machine learning in which you create algorithms with a layered architecture.
DL employs multiple layers to extract higher level characteristics from raw input.
In image processing, for example, lower layers may recognise boundaries, while higher layers may identify concepts meaningful to humans, such as digits, characters, or faces.
Deep learning is sometimes referred to as a deep artificial neural network, and these are algorithm sets that are exceptionally accurate for problems such as sound detection, image recognition, natural language processing, and so on.

To summarise, Data Science comprises AI and machine learning.
However, machine learning encompasses another sub-technology known as deep learning.
Thanks to AI, which is capable of handling increasingly difficult issues (such as detecting cancer better than oncologists) than humans.

Cinoy M R is a Dubai-based Business Architect with extensive experience in technology and business outcome solutions.
He has a Bachelors in Technology (Computing) degree from Thompson Rivers University (TRU), Canada, as well as a Post Graduation in Business Management and a Masters in Business Management (SAP).