Machine Learning Vs Deep Learning Vs Artificial Intelligence Vs Data Science

What is Machine Learning?

☛Machine learning is the ability of a computer system to learn from the environment and improve itself from experience without the need for any explicit programming. Machine learning focuses on enabling algorithms to learn from the data provided, gather insights and make predictions on previously unanalyzed data using the information gathered. The three basic models of machine learning are supervised, unsupervised and reinforcement learning.

☛Generally, there are 3 types of learning algorithms:

a. Supervised Machine Learning Algorithms To make predictions we use this machine learning algorithm. Further, this algorithm searches for patterns within the value labels. That was assigned to data points.

b. Unsupervised Machine Learning Algorithms No labels are associated with data points. Also, these Machine Learning algorithms organize the data into a group of clusters. Moreover, it needs to describe its structure. Also, to make complex data look simple and organized for analysis.

c. Reinforcement Machine Learning Algorithms We use these algorithms to choose an action. Also, we can see that it is based on each data point. Moreover, after some time the algorithm changes its strategy to learn better. Also, achieve the best reward.

What Is Deep Learning?

Machine learning focuses only on solving real-world problems. Also, it takes a few ideas of artificial intelligence. Moreover, machine learning does through the neural networks. That are designed to mimic human decision-making capabilities.
Machine Learning tools and techniques are the two key narrow subsets. That only focuses more on deep learning. Furthermore, we need to apply it to solve any problem. That requires thought- human or artificial.
Any Deep neural network will consist of three types of layers:

☛The Input Layer
☛The Hidden Layer
☛The Output Layer

What is Artificial Intelligence?

Artificial intelligence refers to the simulation of human brain activities by machines. It is a discipline where we make a machine try to analyze the situations and make an inference out of it.
Artificial intelligence is classified into two parts, general AI and Narrow AI. General AI refers to making machines intelligent in a wide array of activities that involve thinking and reasoning. Narrow AI, on the other hand, involves the use of artificial intelligence for a very specific task. For instance, general AI would mean an algorithm that is capable of playing all kinds of board game while narrow AI will limit the range of machine capabilities to a specific game like chess or scrabble.

Basically, Artificial intelligence is a very broad term. Also, it is an attempt to make computers think like human beings. Moreover, any technique, code or algorithm that enables machines to develop. Also, behaviors fall under this category.
As we must be aware that an artificial intelligence system can be as simple as a software that plays chess. It doesn’t matter how complex the system, artificial intelligence is only in its nascent stages.

1- Machine Learning versus Deep Learning

Before digging deeper into the link between data science and machine learning, let’s briefly discuss machine learning and deep learning. Machine learning is a set of algorithms that train on a data set to make predictions or take actions in order to optimize some systems. For instance, supervised classification algorithms are used to classify potential clients into good or bad prospects, for loan purposes, based on historical data. The techniques involved, for a given task (e.g. supervised clustering), are varied: naive Bayes, SVM, neural nets, ensembles, association rules, decision trees, logistic regression, or a combination of many. For a detailed list of algorithms. For a list of machine learning problems,
All of this is a subset of data science. When these algorithms are automated, as in automated piloting or driver-less cars, it is called AI, and more specifically, deep learning. for another article comparing machine learning with deep learning. If the data collected comes from sensors and if it is transmitted via the Internet, then it is machine learning or data science or deep learning applied to IoT.

Some people have a different definition for deep learning. They consider deep learning as neural networks (a machine learning technique) with a deeper layer. The question was asked on Quora recently, and below is a more detailed explanation

★AI (Artificial intelligence) is a sub-field of computer science, that was created in the 1960s, and it was (is) concerned with solving tasks that are easy for humans, but hard for computers. In particular, a so-called Strong AI would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic, and includes all kinds of tasks, such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc.

★NLP (Natural language processing) is simply the part of AI that has to do with language (usually written).
-Machine learning is concerned with one aspect of this: given some AI problem that can be described in discrete terms (e.g. out of a particular set of actions, which one is the right one), and given a lot of information about the world, figure out what is the “correct” action, without having the programmer program it in. Typically some outside process is needed to judge whether the action was correct or not. In mathematical terms, it’s a function: you feed in some input, and you want it to to produce the right output, so the whole problem is simply to build a model of this mathematical function in some automatic way. To draw a distinction with AI, if I can write a very clever program that has human-like behavior, it can be AI, but unless its parameters are automatically learned from data, it’s not machine learning.

★Deep learning is one kind of machine learning that’s very popular now. It involves a particular kind of mathematical model that can be thought of as a composition of simple blocks (function composition) of a certain type, and where some of these blocks can be adjusted to better predict the final outcome.

What is the difference between machine learning and statistics?

This article tries to answer the question. statistics is machine learning with confidence intervals for the quantities being predicted or estimated. I tend to disagree, as I have built engineer-friendly confidence intervals that don’t require any mathematical or statistical knowledge.

2- Data Science versus Machine Learning

Machine learning and statistics are part of data science. The word learning in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters. This encompasses many techniques such as regression, naive Bayes or supervised clustering. But not all techniques fit in this category. For instance, unsupervised clustering – a statistical and data science technique – aims at detecting clusters and cluster structures without any a-priori knowledge or training set to help the classification algorithm. A human being is needed to label the clusters found. Some techniques are hybrid, such as semi-supervised classification. Some pattern detection or density estimation techniques fit in this category.
Data science is much more than machine learning though. Data, in data science, may or may not come from a machine or mechanical process (survey data could be manually collected, clinical trials involve a specific type of small data) and it might have nothing to do with learning as I have just discussed. But the main difference is the fact that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. In particular, data science also covers
☛data integration
☛distributed architecture
☛automating machine learning
☛data visualization
☛dashboards and BI
☛data engineering
☛deployment in production mode
☛automated, data-driven decisions

Of course, in many organisations, data scientists focus on only one part of this process.

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