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Exercise: Perform EDA on Haberman dataset

Instructor: Applied AI Course Duration: 4 mins

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Multivariate Probability Density, Contour Plot
Why learn it ?

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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
3.1
Introduction to Probability and Statistics

17 min
3.2
Population and Sample

17 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
5 min
3.6
Standard normal variate (z) and standardization
15 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
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
3 min
3.20
Confidence interval (C.I) 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
17 min
3.24
Hypothesis testing methodology, Null-hypothesis, p-value
16 min
3.25
Resampling and permutation test
15 min
3.26
K-S Test
6 min
3.27
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}
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