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Why learn it ?

Instructor: Applied AI Course Duration: 4 mins

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Exercise: Perform EDA on Haberman dataset
Introduction to Vectors(2-D, 3-D, n-D), Row Vector and Column Vector

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1.1
Python, Anaconda and relevant packages installations

23 min
1.2
Why learn Python?

4 min
1.3
Keywords and identifiers

6 min
1.4
comments, indentation and statements

9 min
1.5
Variables and data types in Python

32 min
1.6
Standard Input and Output

7 min
1.7
Operators

14 min
1.8
Control flow: if else

10 min
1.9
Control flow: while loop

16 min
1.10
Control flow: for loop

15 min
1.11
Control flow: break and continue

10 min
2.1
Tuples part-1

10 min
3.1
Introduction to IRIS dataset and 2D scatter plot

26 min
3.2
3D scatter plot

6 min
3.3
Pair plots

14 min
3.4
Limitations of pair plots

2 min
3.5
Histogram and Introduction to PDF(Probability Density Function)

17 min
3.6
Univariate Analysis using PDF

6 min
3.7
CDF(Cumulative Distribution Function)

15 min
3.8
Mean, Variance and Standard Deviation

17 min
3.9
Median

10 min
3.10
Percentiles and Quantiles

9 min
3.11
Box-plot with Whiskers

9 min
3.12
Violin Plots

4 min
3.13
Summarizing Plots, Univariate, Bivariate and Multivariate analysis

6 min
3.14
Multivariate Probability Density, Contour Plot

9 min
3.15
Exercise: Perform EDA on Haberman dataset

4 min
5.1
Geometric intuition

13 min