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How to use hypothesis testing?

Instructor: Applied AI Course Duration: 23 mins

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Resampling and Permutation test: another example

Proportional Sampling

Plotting for exploratory data analysis (EDA)

1.1

Introduction to IRIS dataset and 2D scatter plot

26 min

1.2

3D scatter plot

6 min

1.3

Pair plots

14 min

1.4

Limitations of Pair Plots

2 min

1.5

Histogram and Introduction to PDF(Probability Density Function)

17 min

1.6

Univariate Analysis using PDF

6 min

1.7

CDF(Cumulative Distribution Function)

15 min

1.8

Mean, Variance and Standard Deviation

17 min

1.9

Median

10 min

1.10

Percentiles and Quantiles

9 min

1.11

IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)

6 min

1.12

Box-plot with Whiskers

9 min

1.13

Violin Plots

4 min

1.14

Summarizing Plots, Univariate, Bivariate and Multivariate analysis

6 min

1.15

Multivariate Probability Density, Contour Plot

9 min

1.16

Assignment-1: Data Visualization with Haberman Dataset

4 min

Linear Algebra

2.1

Why learn it ?

4 min

2.2

Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector

14 min

2.3

Dot Product and Angle between 2 Vectors

14 min

2.4

Projection and Unit Vector

5 min

2.5

Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane

23 min

2.6

Distance of a point from a Plane/Hyperplane, Half-Spaces

10 min

2.7

Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)

7 min

2.8

Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)

6 min

2.9

Square ,Rectangle

6 min

2.10

Hyper Cube,Hyper Cuboid

3 min

2.11

Revision Questions

30 min

Probability and Statistics

3.1

Introduction to Probability and Statistics

17 min

3.2

Population and Sample

7 min

3.3

Gaussian/Normal Distribution and its PDF(Probability Density Function)

27 min

3.4

CDF(Cumulative Distribution function) of Gaussian/Normal distribution

11 min

3.5

Symmetric distribution, Skewness and Kurtosis

25 min

3.6

Standard normal variate (Z) and standardization

6 min

3.7

Kernel density estimation

7 min

3.8

Sampling distribution & Central Limit theorem

19 min

3.9

Q-Q plot:How to test if a random variable is normally distributed or not?

23 min

3.10

How distributions are used?

17 min

3.11

Chebyshev’s inequality

20 min

3.12

Discrete and Continuous Uniform distributions

13 min

3.13

How to randomly sample data points (Uniform Distribution)

10 min

3.14

Bernoulli and Binomial Distribution

11 min

3.15

Log Normal Distribution

12 min

3.16

Power law distribution

12 min

3.17

Box cox transform

12 min

3.18

Applications of non-gaussian distributions?

26 min

3.19

Co-variance

14 min

3.20

Pearson Correlation Coefficient

13 min

3.21

Spearman Rank Correlation Coefficient

7 min

3.22

Correlation vs Causation

5 min

3.23

How to use correlations?

13 min

3.24

Confidence interval (C.I) Introduction

8 min

3.25

Computing confidence interval given the underlying distribution

11 min

3.26

C.I for mean of a random variable

14 min

3.27

Confidence interval using bootstrapping

18 min

3.28

Hypothesis testing methodology, Null-hypothesis, p-value

16 min

3.29

Hypothesis Testing Intution with coin toss example

27 min

3.30

Resampling and permutation test

15 min

3.31

K-S Test for similarity of two distributions

15 min

3.32

Code Snippet K-S Test

6 min

3.33

Hypothesis testing: another example

18 min