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When we calculate, the output is -0.5. Since this is below the threshold of 0, the result indicates that you will not
go to the playground because it is small, and you will not play cricket due to the absence of a cricket kit.
Perceptron: Go for Picnic
NO(0)
Should I bring a cricket kit?
3.5
12 I N G O O
B 12 8 B I N G 8 28 15
B I N G O
8 28
Should I bring a Tambola 12 60 67 60 67 39 26 9 28 15 2 15 2 9
24 39 26
60 67 39 26 9
FLAT
24 24 1 11 11 11 BACKGROUND47 63 1 YES(1)
FLAT 47 63
FLAT
BINGO
BINGO
BINGO
BACKGROUND
BACKGROUND 47 62
1 48 38 22
48 38 22 5
game to play? 2.5 1 26 54 62 62 13 13 13 77 77 5
48 38 22 5
26 54 62
26
www.bingobackground.com 54
www.bingobackground.com
www.bingobackground.com 77
Output=(0*3.5)+ (1*2.5)+(0*1.5)+(1*1)–(1*4)
1.5
Will there be big playground Output=–0.5
NO(0)
to play cricket?
1.0
4.0
Will there be food outlets B YES(1)
nearby?
YES(1)
Scenario 2
At a Glance
• Artificial Intelligence (AI) is a fast-growing technology that allows machines to do tasks that usually need human
intelligence, like learning, solving problems, and making decisions.
• ML stands for Machine Learning, which is a subset of Artificial Intelligence. It uses statistical methods to enable
machines to learn and improve from experience without being explicitly programmed
• DL stands for Deep Learning. It is a subset of Machine Learning inspired by the structure and function of neurons
in the human brain, leading to the development of Artificial Neural Networks (ANNs).
• Marked or tagged data, which easily identifiable is called labelled data.
• Data that is not marked/tagged is called unlabelled data.
• A collection of data provided to a machine learning model to help it analyse and learn patterns is called training data.
• The testing data set is a collection of data provided to a machine learning model to evaluate how well it has learned
to make predictions.
• AI Modelling refers to developing algorithms, also called models which can be trained to get intelligent outputs.
That is, writing codes to make a machine artificially intelligent. The model is trained using data.
• Supervised Learning is a type of machine learning where a model is trained on a labelled dataset.
• Unsupervised learning approach works on an unlabelled dataset. The machine receives random data with no prior
knowledge available to the trainer.
• Reinforcement Learning is a type of machine learning where a model learns through trial and error to make the
best decisions in a given environment.
• Classification is a rule-based AI model that groups data into categories
• Regression algorithms predict continuous values (e.g., income, house price, temperature, car price).
• Clustering is a machine learning technique that divides a dataset into distinct clusters or categories using algorithmic
patterns.
• Association is an unsupervised learning method that is used to find interesting relationships or patterns among
variables in a dataset.
• Neural Networks are loosely modelled after how neurons in the human brain behave. The key advantage of Neural
Networks is that they are able to extract data features automatically without needing the input of the programmer.
Advanced Concepts of Modeling in AI 133

