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Distance of a point from a Plane/Hyperplane, Half-Spaces
Distance of a point from a Plane/Hyperplane, Half-Spaces
Instructor:
Applied AI Course
Duration:
10 mins
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Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector
Dot Product and Angle between 2 Vectors -
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Python for Data Science Introduction
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
Plotting for exploratory data analysis (EDA)
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
Linear Algebra
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
Interview Questions on Linear Algebra
4.1
Questions & Answers
30 min
Probability and Statistics
5.1
Gaussian/Normal Distribution and its PDF(Probability Density Function)
27 min
5.2
CDF(Cumulative Distribution function) of Gaussian/Normal distribution
11 min
5.3
Co-variance
14 min
Interview Questions on Probability and statistics
6.1
Questions & Answers
30 min
Dimensionality reduction and Visualization:
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
Interview Questions on Dimensionality Reduction
8.1
Questions & Answers
30 min
PCA(principal component analysis)
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
(t-SNE)T-distributed Stochastic Neighbourhood Embedding
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
Interview Questions on K-NN(K Nearest Neighbour)
11.1
Questions & Answers
30 min
Interview Questions on Classification algorithms in various situations
12.1
Questions & Answers
30 min
Interview Questions on Performance Measurement Models
13.1
Questions & Answers
30 min
Interview Questions on Naive Bayes Algorithm
14.1
Questions & Answers
30 min
Solving optimization problems
15.1
Interview Questions on Logistic Regression and Linear Regression
30 min
Interview Questions on Logistic Regression and Linear Regression
16.1
Questions & Answers
30 min
Interview Questions on Support Vector Machine
17.1
Questions & Answers
30 min
Interview Questions on decision Trees
18.1
Questions & Answers
30 min
Interview Questions on Ensemble Models
19.1
Questions & Answers
30 min
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