Data Science VS Machine Learning and Artificial Intelligence

By Tech-Act    
11/23/2020  1325 Views

Data Science VS Machine Learning and Artificial Intelligence

Data science, Machine Learning and Artificial Intelligence, they all belong from the same domain and are interconnected however each of them do have very specific meaning and application.  Even though they may overlap yet they have unique features and uses.

Today in this blog we will point out the difference between Data science, Machine Learning and Artificial Intelligence. But before we look at how they are different from each other, will understand each of them in detail.

  • Data Science.
  • Artificial Intelligence.
  • Machine Learning.
  • Difference between Data Science, Artificial Intelligence & Machine Learning.

Data Science:

Data science is an inter-disciplinary field. It includes broad study of data systems and processes which are carried out to extract knowledge and insights from many structured and unstructured data.  In order to extricate the valuable meaning from the randomly collected data tools, applications, principles and algorithms are used by data scientist. Today organizations globally are producing lot of data which are quite difficult to manage and store and that’s why data science plays an important role as it places emphasis on data modeling and data warehousing to track the increasing data set. By using data science application, information is extracted which is very essential for leading the business processes & for achieving business goals. Business Intelligence is the domain which is highly influenced by data science. The fundamental role of data scientist is to predict and analyze the patterns and trends from the enormous data and this eventually helps in drawing the logical inferences. The historical data are analyzed by data scientist as per the requirement by using different formats like predictive causal analytics & prescriptive analysis. The technologies used are SQL, Python, R and Hadoop. Statistical analysis, Data visualization, Distributed architecture are also used at a large scale for drawing the meaning from the sets of data.

Artificial Intelligence:

Artificial Intelligence is the future and we can clearly see that as people love Google Alex & IPhone Siri. So what is artificial intelligence? Artificial Intelligence is the intelligence which is demonstrated by machines which is of course very different from the natural intelligence that is shown by humans and animals. Artificial Intelligence enables machines to enact reasoning by cloning human intelligence. AI experts highly depend on deep learning and natural language processing to assist machines in identifying patterns and conclusions. AI automates the repetitive and monotonous task by setting up reliable systems that frequently runs the application. It converts conventional products into smart commodities. If you want to improve the technologies of bot and other smart machines then AI should be paired with conversational platform. AI algorithms enable machines to execute different functions as the algorithms act as predictors and classifiers. Also, it is extremely important to analyze and identify the right set of data because machine learns from the data that we feed them.

Machine Learning:

Machine Learning is an implementation of artificial intelligence which provides systems the ability to automatically learn and improve from the experience. The focus of machine learning is to develop the computer program that can access the data and can use it for learning. The main aim of machine learning is to automate the computer learning without any human interference. The algorithms of machine learning are generally classified as supervised or unsupervised.

  • The supervised machine learning algorithms applies historic learning to new data by using labeled instances for predicting future events. Right from the analysis of a known training dataset, inferred functions are produced by learning algorithms to predict the output values. In this method, the learning algorithms compares its produced output with the actual intended output, it locates the error for modifying the model accordingly.
  • Whereas unsupervised algorithms are used when the information is neither classified nor labeled. In this method, the system cannot figure out the correct output but it can draw conclusions from datasets.
  • Semi-Supervised machine learning algorithms is somewhat a blend of both as it uses smaller amount of labeled data and a larger amount of unlabeled data. By using this method, systems can improve their learning accuracy.
  • Reinforcement machine learning algorithms is a learning method in which the interaction happens with its environment by producing actions and exploring errors or rewards. This method enables machines to spontaneously regulate the ideal behavior within the specific context to boost the performance.

Data Science VS Machine Learning and Artificial Intelligence

Artificial IntelligenceMachine LearningData Science
It includes Machine LearningIt is a subset of Artificial LearningIt includes various data operations
In order to automate computers learning, AI amalgamates huge amount of data through iterative processing and intelligent algorithmsML does programming which is efficient enough to use data without being directly told to do so.In order to draw logical inferences from the data, sourcing, cleaning and processing of data is done.
The popular tools used are TensorFlow2, Scikit Learn & KerasThe popular tools used are Amazon Lex2, IBM Watson Studio3, Microsoft Azure ML studio.The popular tools used are SAS2, Tableau3, Apache, Spark4, MATLAB.
AI uses logic and decision treesML uses statistical modelIt makes use of structured and unstructured data.
The popular applications are AI are Chatbots and Voice assistantsSpotify and Facial recognition are popular recommendation systemsThe ones which are popular in data science are Fraud Detection and Healthcare Analysis.

Summary:

We all talk about these modern technologies i.e. artificial intelligence, machine learning and data science but very few of us really understand that what actually these technologies are & how they can change human life. So we hope that this blog would have erased your ambiguities in understanding what these technologies are and how they are unique from each other.

Thank you! Keep reading.

Data science, Machine Learning and Artificial Intelligence, they all belong from the same domain and are interconnected however each of them do have very specific meaning and application.  Even though they may overlap yet they have unique features and uses.

Today in this blog we will point out the difference between Data science, Machine Learning and Artificial Intelligence. But before we look at how they are different from each other, will understand each of them in detail.

  • Data Science.
  • Artificial Intelligence.
  • Machine Learning.
  • Difference between Data Science, Artificial Intelligence & Machine Learning.

Data Science:

Data science is an inter-disciplinary field. It includes broad study of data systems and processes which are carried out to extract knowledge and insights from many structured and unstructured data.  In order to extricate the valuable meaning from the randomly collected data tools, applications, principles and algorithms are used by data scientist. Today organizations globally are producing lot of data which are quite difficult to manage and store and that’s why data science plays an important role as it places emphasis on data modeling and data warehousing to track the increasing data set. By using data science application, information is extracted which is very essential for leading the business processes & for achieving business goals. Business Intelligence is the domain which is highly influenced by data science. The fundamental role of data scientist is to predict and analyze the patterns and trends from the enormous data and this eventually helps in drawing the logical inferences. The historical data are analyzed by data scientist as per the requirement by using different formats like predictive causal analytics & prescriptive analysis. The technologies used are SQL, Python, R and Hadoop. Statistical analysis, Data visualization, Distributed architecture are also used at a large scale for drawing the meaning from the sets of data.

Artificial Intelligence:

Artificial Intelligence is the future and we can clearly see that as people love Google Alex & IPhone Siri. So what is artificial intelligence? Artificial Intelligence is the intelligence which is demonstrated by machines which is of course very different from the natural intelligence that is shown by humans and animals. Artificial Intelligence enables machines to enact reasoning by cloning human intelligence. AI experts highly depend on deep learning and natural language processing to assist machines in identifying patterns and conclusions. AI automates the repetitive and monotonous task by setting up reliable systems that frequently runs the application. It converts conventional products into smart commodities. If you want to improve the technologies of bot and other smart machines then AI should be paired with conversational platform. AI algorithms enable machines to execute different functions as the algorithms act as predictors and classifiers. Also, it is extremely important to analyze and identify the right set of data because machine learns from the data that we feed them.

Machine Learning:

Machine Learning is an implementation of artificial intelligence which provides systems the ability to automatically learn and improve from the experience. The focus of machine learning is to develop the computer program that can access the data and can use it for learning. The main aim of machine learning is to automate the computer learning without any human interference. The algorithms of machine learning are generally classified as supervised or unsupervised.

  • The supervised machine learning algorithms applies historic learning to new data by using labeled instances for predicting future events. Right from the analysis of a known training dataset, inferred functions are produced by learning algorithms to predict the output values. In this method, the learning algorithms compares its produced output with the actual intended output, it locates the error for modifying the model accordingly.
  • Whereas unsupervised algorithms are used when the information is neither classified nor labeled. In this method, the system cannot figure out the correct output but it can draw conclusions from datasets.
  • Semi-Supervised machine learning algorithms is somewhat a blend of both as it uses smaller amount of labeled data and a larger amount of unlabeled data. By using this method, systems can improve their learning accuracy.
  • Reinforcement machine learning algorithms is a learning method in which the interaction happens with its environment by producing actions and exploring errors or rewards. This method enables machines to spontaneously regulate the ideal behavior within the specific context to boost the performance.

Data Science VS Machine Learning and Artificial Intelligence

Artificial IntelligenceMachine LearningData Science
It includes Machine LearningIt is a subset of Artificial LearningIt includes various data operations
In order to automate computers learning, AI amalgamates huge amount of data through iterative processing and intelligent algorithmsML does programming which is efficient enough to use data without being directly told to do so.In order to draw logical inferences from the data, sourcing, cleaning and processing of data is done.
The popular tools used are TensorFlow2, Scikit Learn & KerasThe popular tools used are Amazon Lex2, IBM Watson Studio3, Microsoft Azure ML studio.The popular tools used are SAS2, Tableau3, Apache, Spark4, MATLAB.
AI uses logic and decision treesML uses statistical modelIt makes use of structured and unstructured data.
The popular applications are AI are Chatbots and Voice assistantsSpotify and Facial recognition are popular recommendation systemsThe ones which are popular in data science are Fraud Detection and Healthcare Analysis.

Summary:

We all talk about these modern technologies i.e. artificial intelligence, machine learning and data science but very few of us really understand that what actually these technologies are & how they can change human life. So we hope that this blog would have erased your ambiguities in understanding what these technologies are and how they are unique from each other.

Thank you! Keep reading.


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