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Dot Product and Angle between 2 Vectors

Instructor: Applied AI Course Duration: 14 mins

If we take both as row vectors , so a.b => a.b_T , By default, we assume all vectors to be column vectors unless otherwise stated to avoid confusion. So, a.b =a_T*b = b_T*a

Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector

Projection and Unit Vector

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How to utilise Appliedaicourse

1.1

How to Learn from Appliedaicourse

35 min

Python for Data Science Introduction

2.1

Python, Anaconda and relevant packages installations

23 min

2.2

Why learn Python?

4 min

2.3

Keywords and identifiers

6 min

2.4

comments, indentation and statements

9 min

2.5

Variables and data types in Python

32 min

2.6

Standard Input and Output

7 min

2.7

Operators

14 min

2.8

Control flow: if else

10 min

2.9

Control flow: while loop

16 min

2.10

Control flow: for loop

15 min

2.11

Control flow: break and continue

10 min

Python for Data Science: Data Structures

3.1

Lists

38 min

3.2

Tuples part 1

10 min

3.3

Tuples part-2

4 min

3.4

Sets

16 min

3.5

Dictionary

21 min

3.6

Strings

16 min

Plotting for exploratory data analysis (EDA)

4.1

Introduction to IRIS dataset and 2D scatter plot

26 min

4.2

3D scatter plot

6 min

4.3

Pair plots

14 min

4.4

Limitations of Pair Plots

2 min

4.5

Histogram and Introduction to PDF(Probability Density Function)

17 min

4.6

Univariate Analysis using PDF

6 min

4.7

CDF(Cumulative Distribution Function)

15 min

4.8

Mean, Variance and Standard Deviation

17 min

4.9

Median

10 min

4.10

Percentiles and Quantiles

9 min

4.11

IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)

6 min

4.12

Box-plot with Whiskers

9 min

4.13

Violin Plots

4 min

4.14

Summarizing Plots, Univariate, Bivariate and Multivariate analysis

6 min

4.15

Multivariate Probability Density, Contour Plot

9 min

4.16

Assignment-1: Data Visualization with Haberman Dataset

4 min

Linear Algebra

5.1

Why learn it ?

4 min

5.2

Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector

14 min

5.3

Dot Product and Angle between 2 Vectors

14 min

5.4

Projection and Unit Vector

5 min

5.5

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

23 min

5.6

Distance of a point from a Plane/Hyperplane, Half-Spaces

10 min

5.7

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

7 min

5.8

Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)

6 min

5.9

Square ,Rectangle

6 min

5.10

Hyper Cube,Hyper Cuboid

3 min

Probability and Statistics

6.1

Introduction to Probability and Statistics

17 min

6.2

Population and Sample

7 min

6.3

Gaussian/Normal Distribution and its PDF(Probability Density Function)

27 min

6.4

CDF(Cumulative Distribution function) of Gaussian/Normal distribution

11 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: Feature Normalisation

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 Data Set

12 min

PCA(principal component analysis)

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 (PCA): Dimensionality reduction

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

19 min

8.10

PCA for dimensionality reduction (not-visualization)

15 min

(t-SNE)T-distributed Stochastic Neighbourhood Embedding

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

Case Study 1: Quora question Pair Similarity Problem

10.1

Business/Real world problem : Problem definition

6 min

Case Study 2: Personalized Cancer Diagnosis

11.1

Business/Real world problem : Overview

13 min

Case study 4:Taxi demand prediction in New York City

12.1

Business/Real world problem Overview

9 min

12.2

Mapping to ML problem :Fields/Features.

6 min

Case study 5: Stackoverflow tag predictor

13.1

Business/Real world problem

10 min

Case Study 6: Microsoft Malware Detection

14.1

Business/real world problem :Problem definition

6 min

Case Study 9:Netflix Movie Recommendation System (Collaborative based recommendation)

15.1

Business/Real world problem:Problem definition

6 min

OpenCV using Python

16.1

Code Walkthrough (OpenCV using Python)

Case Study 10: Self Driving Car

17.1

Self Driving Car :Problem definition.

14 min

Case Study 13: Semantic Search Engine for Q&A [Design + Code]

18.1

High Level Design of the Solution

18.2

Sentence Vectors and Docker Containerisation

18.3

Indexing using ElasticSearch

18.4

Deployment using Flask APIs, Docker and ElasticSearch

Statistical Testing and Experiments(Recorded LIVE Sessions)

19.1

Introduction to A/B Tests

19.2

How do we randomly split users ?

19.3

Metrics to compare Control and Treatment

19.4

Permutation-Resampling test

19.5

Q&A: Why permute/shuffle?

19.6

Q&A: How would shuffle programatically?

19.7

Quick Recap [till now]

19.8

Q&A + Polls with participants

19.9

Hypothesis testing: p-values, significance level & errors

19.10

Estimating the power of Permutation Tests

19.11

Code Walkthrough: Permutation Test from scratch.

19.12

Simulations using Code

19.13

Q&A with participants

19.14

Poll + Recap [till now]

19.15

Pros and Cons of Permutation Testing

19.16

Variation: Measuring clicks

19.17

Mann-Whitney-U Test

19.18

Code for Mann-Whitney-U test

19.19

Q&A with participants

19.20

Multiple testing and solutions

19.21

Remdesivir for Covid-19: Research Paper

19.22

Q&A with participants

19.23

Quick Recap (till now)

19.24

Bootstrapping for Confidence Intervals

19.25

Introduction to Multi-Arm-Bandits

19.26

Q&A with participants

Module 1: Live sessions

20.1

Code Walkthrough: Basic programming & bug-fixing in Python (for AI)

20.2

Code Walkthrough: Numerical algorithms using Python (for AI)

20.3

Code Walkthrough: Numerical methods in Python (for AI) -II

20.4

Code Walkthrough: Problems in Python [ Strings and Regex ]

20.5

Code Walkthrough: Problems in Python [ Strings and Regex -II]

20.6

Code Walkthrough: Dynamic Programming & Python in-built data-structures

20.7

Code Walkthrough: OOP in Python (for AI)- I

20.8

Code Walkthrough: OOP in Python for AI -II

20.9

How to code effectively and build a web-scraper

20.10

Using Web-APIs in Python for Machine Learning

20.11

How to use Github?

20.12

Multi-Processing & Multithreading in Python for AI/ML

20.13

Parallel programming for training and productionization of ML/AI systems [Flask & Gunicorn]

20.14

SQL: Importance and Sample Problems

20.15

Interactive Interview Session on Python programming for ML/AI

20.16

Smart data acquisition for ML and AI

Module 2: Live Sessions

21.1

Code Walkthrough: Live session on Basics of Linear Algebra for AI/ML

21.2

Code Walkthrough: Dimensionality Reduction for ML/AI

Module 7: Live Sessions

22.1

Building a simple Youtube recommendation using basic Math

Module 8: Live Sessions

23.1

Interactive Interview Questions(from top product based companies)

23.2

Scenario based interview questions in AI/ML/DataScience

Module 9: Live Sessions

24.1

High-level design of a self driving system

Machine Learning High-Level Design

25.1

Machine Learning design: Search engine for Q&A

25.2

ML System Design: Feature Store

31 min

Sample Interview and Conceptual Questions [AUDIO]

26.1

Sample Interview and Conceptual Questions [AUDIO]

Module 10: Live Sessions

27.1

An overview of AI Algorithms ( a 10,000 feet view)

27.2

Big-Data & Cloud Storage for ML/AI Applications

27.3

Spark for Data Science and Machine Learning [Architecture and Programming model]- I

27.4

Design and build a Chatbot from Scratch

27.5

Design and build a Chatbot from Scratch-Part 2

27.6

How to build IoT + AI systems

27.7

Sample Interview and Conceptual Questions [AUDIO]

Dot Product and Angle between 2 Vectors

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