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

Instructor: Applied AI Course Duration: 27 mins

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CDF(Cumulative Distribution function) of Gaussian/Normal distribution

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1.1
Why learn Python?

4 min
1.2
Keywords and identifiers

6 min
1.3
comments, indentation and statements

9 min
1.4
Variables and data types in Python

32 min
1.5
Standard Input and Output

7 min
1.6
Operators

14 min
1.7
Control flow: if else

10 min
1.8
Control flow: while loop

16 min
1.9
Control flow: for loop

15 min
1.10
Control flow: break and continue

10 min
1.11
Python, Anaconda and relevant packages installations

23 min
2.1
Introduction to IRIS dataset and 2D scatter plot

26 min
2.2
3D scatter plot

6 min
2.3
Pair plots

14 min
2.4
Limitations of pair plots

2 min
2.5
Histogram and Introduction to PDF(Probability Density Function)

17 min
2.6
Univariate Analysis using PDF

6 min
2.7
CDF(Cumulative Distribution Function)

15 min
2.8
Mean, Variance and Standard Deviation

17 min
2.9
Median

10 min
2.10
Percentiles and Quantiles

9 min
2.11
IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)

6 min
2.12
Box-plot with Whiskers

9 min
2.13
Violin Plots

4 min
2.14
Summarizing Plots, Univariate, Bivariate and Multivariate analysis

6 min
2.15
Multivariate Probability Density, Contour Plot

9 min
2.16
Exercise: Perform EDA on Haberman dataset

4 min
3.1
Why learn it ?

4 min
3.2
Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector

14 min
3.3
Distance of a point from a Plane/Hyperplane, Half-Spaces

10 min
3.4
Dot Product and Angle between 2 Vectors -

14 min
3.5
Projection and Unit Vector

5 min
3.6
Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane

23 min
3.7
Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)

7 min
3.8
Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)

6 min
3.9
Hyper Cube,Hyper Cuboid

3 min
3.10
Assignment[Optional]

30 min
4.1
Questions & Answers

30 min
6.1
Questions & Answers

30 min
7.1
what is dimensionality reduction?

3 min
7.2
Row Vector and Column Vector

5 min
7.3
How to represent a data set?

4 min
7.4
How to represent a dataset as a Matrix.

7 min
7.5
Data preprocessing: Column Normalization

20 min
7.6
Mean of a data matrix

6 min
7.7
Data preprocessing: Column Standardization

16 min
7.8
Co-variance of a Data Matrix

24 min
7.9
MNIST dataset (784 dimensional)

20 min
7.10
Code to load MNIST Dataset

12 min
8.1
Questions & Answers

30 min
9.1
Why learn PCA?

4 min
9.2
Geometric intuition of PCA

14 min
9.3
Mathematical objective function of PCA

13 min
9.4
Alternative formulation of PCA: Distance minimization

10 min
9.5
Eigen values and Eigen vectors

23 min
9.6
PCA for Dimensionality Reduction and Visualization

10 min
9.7
Visualize MNIST dataset

5 min
9.8
Limitations of PCA

5 min
9.9
PCA Code example using Visualization

19 min
9.10
PCA Code example using non-Visualization

15 min
10.1
What is t-SNE?

7 min
10.2
Neighborhood of a point, Embedding

7 min
10.3
Geometric intuition of t-SNE

9 min
10.4
Crowding Problem

8 min
10.5
How to apply t-SNE and interpret its output

38 min
10.6
t-SNE on MNIST

7 min
10.7
Code example of t-SNE

9 min
11.1
Questions & Answers

30 min
12.1
Questions & Answers

30 min
13.1
Questions & Answers

30 min
14.1
Questions & Answers

30 min
15.1
Interview Questions on Logistic Regression and Linear Regression

30 min
16.1
Questions & Answers

30 min
17.1
Questions & Answers

30 min
18.1
Questions & Answers

30 min
19.1
Questions & Answers

30 min