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

Instructor: Applied AI Course Duration: 27 mins

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Population and Sample
CDF(Cumulative Distribution function) of Gaussian/Normal distribution

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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
Exercise: Perform EDA on Haberman dataset

4 min
2.1
Why learn it ?

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
4.1
what is dimensionality reduction?
3 min
4.2
Row vector, Column vector: Iris dataset example
5 min
4.3
Represent a dataset: D= {x_i, y_i}
5 min
4.4
Represent a dataset as a Matrix.
7 min
4.5
Data preprocessing: Column Normalization
20 min
4.6
Mean of a data matrix
6 min
4.7
Data preprocessing: Column Standardization
6 min
4.8
Co-variance of a Data Matrix
24 min
4.9
MNIST dataset (784 dimensional)
20 min
4.10
Code to load MNIST dataset
12 min
5.1
Why learn it.
4 min
5.2
Geometric intuition
14 min
5.3
Mathematical objective function.
13 min
5.4
Alternative formulation of PCA: distance minimization
10 min
5.5
Eigenvalues and eigenvectors.
23 min
5.6
PCA for dimensionality reduction and visualization.
10 min
5.7
Visualize MNIST dataset.
5 min
5.8
Limitations of PCA
5 min
5.9
Code example using visualization
19 min
5.10
PCA for dimensionality reduction (not-visualization)
15 min
6.1
What is t-SNE?
7 min
6.2
Neighborhood of a point, Embedding
7 min
6.3
Geometric intuition.
9 min
6.4
Crowding problem.
8 min
6.5
How to apply t-SNE and interpret its output (distill.pub)
38 min
6.6
t-SNE on MNIST.
7 min
6.7
Code example.
9 min