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AI: Artificial Intelligence
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ML: Machine Learning
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DL: Deep Learning
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DS: Data Science.
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AI: enable machine to think. That means without
any human intervention machine will take decision.
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E.g Driverless CAR
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ML: Machine learning is subset of AI, provide
statistical tools to explore and analyze the data
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There are 3 different approaches
ü Supervised
ü Un-supervised
ü Reinforcement
learning / semi supervised ML
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Supervised: we will have data in hand to be
processed, past labeled data and we know what will be output of data
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Un-supervised: we don’t have data, we solve
clustering problem. Clustering mean
based on similarity of data it will try to group data together. We have 3 different
algorithm K Means clustering, db scan clustering, Hierarchical clustering
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Reinforcement learning / semi supervised ML: Some
part of data will be labeled, and some will not be learned, in this model computer
learn slowly using past data and new data
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Deep Learning: Multineural network architecture,
make machine to learn like human.
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ANN : Artificial neural network , if data is in
form of number we use ANN
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CNN : Convolutional neural network , if data is in form of images we use CNN
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TL (Advanced Neural network based on CNN)
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RNN : Recurrent neural network , if our data is in form of time series data
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Data Science: use all above technique and use mathematical
tool like statistic, probability and linear analyses
