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

Instructor: Applied AI Course Duration: 7 mins

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

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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
3.1
Questions & Answers

30 min
4.1
Introduction to Probability and Stats
17 min
4.2
Population and Sample
7 min
4.3
Gaussian/Normal Distribution and its PDF(Probability Density Function)

27 min
4.4
CDF(Cumulative Distribution function) of Gaussian/Normal distribution

11 min
4.5
Symmetric distribution, Skewness and Kurtosis
25 min
4.6
Standard normal standardization
5 min
4.7
Kernel density estimation
7 min
4.8
Sampling distribution & Central Limit theorem
19 min
4.9
Q-Q plot:How to test if a random variable is normally distributed or not?
23 min
4.10
Discrete and Continuous Uniform distributions
13 min
4.11
How to randomly sample data points (Uniform Distribution)
10 min
4.12
Bernoulli and Binomial Distribution
11 min
4.13
Log Normal Distribution
12 min
4.14
Power law distribution
12 min
4.15
Box cox transform
12 min
4.16
Co-variance

14 min
4.17
Pearson Correlation Coefficient
13 min
4.18
Spearman Rank Correlation Coefficient
7 min
4.19
Correlation vs Causation
3 min
4.20
Confidence interval (C.I) Introduction
8 min
4.21
Discrete and Continuous Uniform distributions
11 min
4.22
C.I for mean of a normal random variable
14 min
4.23
Confidence interval using bootstrapping
17 min
4.24
Hypothesis testing methodology, Null-hypothesis, p-value
16 min
4.25
Resampling and permutation test
15 min
4.26
K-S Test for similarity of distributions
15 min
4.27
K-S Test for similarity of two distributions
6 min
4.28
Hypothesis Testing Intution with coin toss example
27 min
4.29
Hypothesis testing Mean differences Example
18 min
5.1
Questions & Answers

30 min
6.1
what is dimensionality reduction?

3 min
6.2
Row Vector and Column Vector

5 min
6.3
How to represent a data set?

4 min
6.4
How to represent a dataset as a Matrix.

7 min
6.5
Data preprocessing: Column Normalization

20 min
6.6
Mean of a data matrix

6 min
6.7
Data preprocessing: Column Standardization

16 min
6.8
Co-variance of a Data Matrix

24 min
6.9
MNIST dataset (784 dimensional)

20 min
6.10
Code to load MNIST Dataset

12 min
7.1
Questions & Answers

30 min
8.1
Why learn PCA?

4 min
8.2
Geometric intuition of PCA

14 min
8.3
Mathematical objective function of PCA

13 min
8.4
Alternative formulation of PCA: Distance minimization

10 min
8.5
Eigen values and Eigen vectors

23 min
8.6
PCA for Dimensionality Reduction and Visualization

10 min
8.7
Visualize MNIST dataset

5 min
8.8
Limitations of PCA

5 min
8.9
PCA Code example using Visualization

19 min
8.10
PCA Code example using non-Visualization

15 min
9.1
What is t-SNE?

7 min
9.2
Neighborhood of a point, Embedding

7 min
9.3
Geometric intuition of t-SNE

9 min
9.4
Crowding Problem

8 min
9.5
How to apply t-SNE and interpret its output

38 min
9.6
t-SNE on MNIST

7 min
9.7
Code example of t-SNE

9 min
9.8
Assignment
30 min