Artificial Intelligence and Machine Learning using Python
Artificial Intelligence and Machine Learning using Python
The online modules under IEM America Winter School
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1
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1.Introduction to the course
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2.Human learning and it's types
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3.Machine learning and it's types
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4.Process of Machine Learning
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5.Well-posed learning problem
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6.Applications of machine learning
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7.QUIZ
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2
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8.Basic data types
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9.Data exploration
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10.Data exploration (continued)
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11.Data issues and remediation
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12.Issues in machine learning
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13.QUIZ
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3
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14.Getting started with Python
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15.Basic Python commands
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16.Basic Python commands (continued)
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17.Functions
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18.QUIZ
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19.Operators
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20.Conditional (IF) Statement
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21.FOR Loops
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22.WHILE Loops
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23.QUIZ
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24.Libraries - os, numpy, pandas, matplotlib
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25.numpy
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26.pandas
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27.Data exploration using matplotlib
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28.Data issues and remediation
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29.QUIZ
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4
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30.What is a model
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31.Selecting a model
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32.Training model - Holdout Method
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33.Training model - k-fold cross-validation
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34.Training model - bootstrap sampling
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35.Model representation and interpretability - under-fitting, over-fitting
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36.QUIZ
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37.Model performance evaluation - Classification
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38.Model performance evaluation - Regression
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39.Model performance evaluation - Clustering
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40.Model performance tuning
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41.QUIZ
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5
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42.Basics of feature engineering
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43.Feature Construction
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44.Feature extraction
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45. Feature selection 1
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46.Feature selection 2
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47.Feature selection 3
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6
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48.supervised learning classification 1
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49.supervised learning classification 2
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50.supervised learning classification 3
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51.supervised learning classification 4
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52.supervised learning classification 5
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53.supervised learning classification 6
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7
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Question Paper - Quiz 1
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ANSWER BOOKLET : QUIZ 1 (For questions, refer to Question Paper - Quiz 1)