Home Courses Yellow taxi Demand prediction Newyork city Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D) - Copy

Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D) - Copy

Instructor: Applied AI Course Duration: 23 mins

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Projection and Unit Vector
Distance of a point from a Plane/Hyperplane, Half-Spaces

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1.1
Introduction to IRIS dataset

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)
30 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
Introduction to Probability and Statistics
17 min
3.2
Population and Sample
7 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?
5 min
4.2
Row Vector and Column Vector
4 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
30 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
30 min
4.9
MNIST dataset (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
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
PCA 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 of t-SNE
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 of t-SNE
9 min