Difference between Artificial Intelligence, Machine Learning, Deep Learning, and Data Science

Artificial Intelligence vs Machine Learning vs Deep Learning vs Data Science

Aviral Bhardwaj
Nov 4, 2021 · 7 min read

see in today’s generation we hear words like Artificial Intelligence, Machine Learning, Deep Learning, and Data Science, and some of them might be working on these technologies or they will work on them in future

so today in this article I would be clearing the difference between Artificial Intelligence, Machine Learning, Deep Learning, and Data Science as many people are confused about it and they think that it all means the same but the answer is no yes a big no

these technologies may sound common thing to you but in reality, they are not in most cases and they are also common in very few scenarios

so let’s start

Artificial Intelligence

It is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and be able make decisions.

Machine Learning

Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed.

Deep Learning

Deep Learning, is just a type of Machine Learning, inspired by the structure of the human brain. Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks.

Data Science

Data science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms.

Artificial Intelligence

everything in this article is actually a part of artificial intelligence but it can be categorized into these categories

Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed.

is just a type of Machine Learning, inspired by the structure of the human brain. Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure.

natural language processing (NLP) is a subfield of artificial intelligence (AI), computer science, and linguistics it helps the machine to interact between computers and human language.

Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment, and it can handle problems with stochastic transitions and rewards without requiring adaptations.

an intelligent agent is anything that perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or may use knowledge.

This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently.

computer vision enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs — and take actions or make recommendations based on that information.

to know more about natural language processing you can check the link — https://becominghuman.ai/what-is-natural-language-processing-nlp-16cc0f94858f

Machine Learning

machine learning is divided into 4 categories

Supervised learning involves learning a function that maps an input to an output based on example input-output pairs.

Supervised learning can be grouped further into two categories of algorithms

  • Classification
  • Regression

Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes.

unsupervised learning can be further classifieds into two categories of algorithms:

  • Clustering
  • Association

Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. The agent learns automatically with this feedbacks and improves its performance.

working of a machine learning model

Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training.

well the main categories are only 3 semi-supervised learning doesn’t come into use as compared to others

to know more about machine learning you can check the link — https://iaviral.medium.com/an-introduction-to-machine-learning-and-artificial-intelligence-505663e3da7f

to know all the machine learning models you can check this link — https://iaviral.medium.com/all-machine-learning-models-explained-a65312aad1b8

to know more about supervised learning and unsupervised learning you can check the link — https://becominghuman.ai/supervised-learning-vs-unsupervised-learning-8af1bc803210

to know more about classification and regression models you can check the link — https://becominghuman.ai/difference-between-classification-and-regression-models-b929445b8d54

Deep Learning

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. neural networks refer to systems of neurons.

a view of neural network

neural networks can be further classifieds into three categories of the neural networks:

Artificial neural networks (ANN), usually simply called neural networks, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons

A convolutional neural network (CNN, or ConvNet) is another class of deep neural network. CNN’s are most commonly employed in computer vision. Given a series of images or videos from the real world, with the utilization of CNN, the AI system learns to automatically extract the features of these inputs to complete a specific task

A recurrent neural network (RNN) is another class of artificial neural networks that uses sequential data feeding. RNNs have been developed to address the time-series problem of sequential input data

to know more about deep learning and neural networks you can check the link — https://becominghuman.ai/what-is-deep-learning-f441713ffb3c

Data Science

data science can be divided into many categories some of them are below

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps. and we have matplotlib library in python so we can plot beautiful representations of our data

data analysis is used to analyze our data cause real-life data is not easy to gather and sometimes we need to make our data more efficient to improve our model performance, accuracy and to gain some useful information from data. we use the pandas library for data analysis

Data manipulation refers to the process of adjusting data to make it organized and easier to read. Data manipulation adjusts data by inserting, deleting, and modifying data in a database such as to cleanse or map the data. we use NumPy in this field.

in the real world, data gathering is very difficult and sometimes data can be messier so we need to clean the data first to improve our model precision and accuracy.

data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise, deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

to know more about data science you can check the link — https://iaviral.medium.com/what-is-data-science-151dfbfe205c

Applications

  • self-driving cars
  • virtual Assistant
  • recommender system
  • prediction models
  • image recognition
  • chatbots
  • internet search
  • Fraud and Risk Detection.

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