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Summarizing Plots, Univariate, Bivariate and Multivariate analysis

Instructor: Applied AI Course Duration: 6 mins

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Violin Plots
Multivariate Probability Density, Contour Plot

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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
Distance of a point from a Plane/Hyperplane, Half-Spaces
6 min
2.9
Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)

6 min
2.10
Square ,Rectangle

3 min
2.11
Hyper Cube,Hyper Cuboid
3 min
3.1
Introduction to Probability and Stats
17 min
3.2
Population and Sample
7 min
3.3
Gaussian/Normal Distribution
27 min
3.4
CDF(Cumulative Distribution function) of Gaussian/Normal distribution
11 min
3.5
Symmetric distribution, Skewness and Kurtosis
5 min
3.6
Standard normal variate (z) and standardization
15 min
3.7
Kernal density estimation
7 min
3.8
Sampling distribution and 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
Discrete and Continuous Uniform distributions
13 min
3.11
How to randomly sample data points (Uniform Distribution)
10 min
3.12
Bernoulli and Binomial Distribution
11 min
3.13
Log Normal Distribution
12 min
3.14
Power law distribution
12 min
3.15
Box cox transform
12 min
3.16
Co-variance
14 min
3.17
Pearson Correlation Coefficient
13 min
3.18
Spearman Rank Correlation Coefficient
7 min
3.19
Correlation vs Causation
7 min
3.20
Confidence Interval Introduction
8 min
3.21
Computing confidence-interval given distribution
11 min
3.22
C.I for mean of a normal random variable
14 min
3.23
Confidence interval using bootstrapping
7 min
3.24
Hypothesis testing methodology, Null-hypothesis, p-value
16 min
3.25
Testing methodology, Null-hypothesis, test-statistic, p-value
16 min
3.26
Resampling and permutation test
15 min
3.27
K-S Test
6 min
3.28
K-S Test for similarity of two distributions
15 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}
4 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
16 min
4.8
Co-variance of a Data Matrix
24 min
4.9
Explanation of the dataset(MNIST Data Set(784 Dimensional))
20 min
4.10
Code to load MNIST dataset
12 min
5.1
Why learn PCA?
4 min
5.2
Geometric intuition of PCA
14 min
5.3
Mathematical objective function of PCA
13 min
5.4
Alternative formulation of PCA: Distance minimization
10 min
5.5
Eigen values and Eigen vectors
23 min
5.6
PCA for Dimensionality Reduction and Visualization
10 min
5.7
Visualize MNIST dataset
5 min
5.8
Limitations of PCA
10 min
5.9
PCA Code example using Visualization
19 min
5.10
PCA Code example using non-Visualization
15 min
6.1
What is t-SNE?
7 min
6.2
Neighborhood of a point, Embedding
7 min
6.3
Geometric intuition of t-SNE
9 min
6.4
Crowding Problem
8 min
6.5
How to apply t-SNE and interpret its output
38 min
6.6
t-SNE on MNIST
7 min
6.7
Code example of t-SNE
9 min