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ARTIFICIAL INTELLIGENCE WITH MACHINE
LEARNING

Course Code Course N am e Artificial I ntelligence w ith m achine
learning

Course Outcom e Construct Machine Learning activities Teac h Mark s
-4 Hrs

Learning Explain m achine learning by exam ples 10 10
Outcom e 1
Contents ● AI vs ML, ML models, ML need & future scope
● Supervised learning: Regression & classification, Linear
Method
Assessm ent regression, logistic regression, KNN
Learning ● Unsupervised Learning: Clustering algorithm (K-means),
Outcom e 2
Contents Market basket analysis
● Reinforcement learning: introduction, types & applications
Method
Assessm ent of External: End semester theory examination (Pen paper test).

Explain dataset for ML processing 6 10

● Define dataset, steps of data processing, data cleaning,

feature extraction

● Prerequisite Steps to Build ML model: Installing libraries

(python scipy), Load the dataset, Summarize dataset,

Perform Data visualization, Evaluate ML algorithm (eg.

Linear regression, KNN), Do Model prediction

● Case study of available ML

model (Kaggle Kernel/ AI.Google)

of Internal –Quiz /Short Answer type questions/Progressive

Test(Pen Paper)

Learning Experim ent w it h ML open source 8 10
Outcom e 3 developm ent environm ent
Contents
● Design ML applications using available ML development
Method
Assessm ent environment (eg. Google Colab, IBM Watson)

● Explore & design ML datasets from various available
sources (Kaggle datasets, UCI machine learning
repository, Google dataset search, OpenML)

of External: Laboratory observation and viva voce.

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ARTIFICIAL INTELLIGENCE WITH MACHINE
LEARNING