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Difference between AI,ML,DL and DS

In this presentation you can learn What is the major difference between artificial intelligence, machine learning, deep leaning and data science.

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Difference between AI,ML,DL and DS

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  1. Difference between AI,ML,DL and DS

  2. Artificial Intelligence (AI) Artificial Intelligence enable the machine to think and act with out any human intervention. AI are into 3 types ANI (Artificial Narrow Intelligence) which is goal- oriented and programmed to perform a single task. For example – automatic washing machine AGI (Artificial General Intelligence) which allows machines to learn, understand, and act in a way that the machine is programmed For example – auto pilot car ASI (Artificial Super Intelligence) is a hypothetical AI where machines are intelligence that surpasses brightest humans. For example – automation robots capable of exhibiting

  3. Machine Learning (ML) Machine Learning (ML) is a subset of artificial intelligence (AI) that uses statistical learning algorithms to build smart machine or model. The (ML) Machine Learning systems can automatically learn and improve without explicit being programmed. Machine Learning (ML) is commonly used along with artificial intelligence (AI) but it is a subset of artificial intelligence (AI). Machine Learning (ML) refers to an artificial intelligence (AI) system that can self learn based on the algorithm. Systems that get smarter and smarter over time without human intervention is Machine Learning (ML). For example - suggestions in youtube videos

  4. Deep learning (DL) Deep learning is also a subset of artificial intelligence. The deep learning is inspired by the way a human brain analyze the information . deep learning help the AI model to analyze the input data to forecast or classify the information. Deep learning analyze the data in multi layer neural network ANN(artificial neural network) information input in the from of numbers CNN( convolution neural network) information input in the from of images RNN(recurrent neural network)information input in the from of time series data

  5. Data Science (DS) It consists of mathematical tools like statistical, operations research techniques, linear algebra, machine learning and deep learning algorithms. The objective of the data science is to identify or analyze the most appropriate statistical algorithm that can be used. model/machine learning Data science can be used to analyze the forecasting, by using data science components those are ·Descriptive analytics what happen in past ·Predictive analytics what will happen in future ·Prescriptive analytics current data visualization or decisionmaking

  6. About Us LearnBay is a Bangalore based Data Science Institute which is dedicated to help students , professionals to become Industry – ready in Data Science. LearnBay provides IBM Certificates in Data Science. Our Key features are : 200 + Hours of classroom sessions Live classes with recording of all the classes Flexibility in Scheduling classes Class strength not more than 10 Highly qualified trainers 12+ Real time project and case studies Job Assistance – Resume building, mock interview and job referrals https://www.learnbay.in/

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