Home Courses Applied Machine Learning Online Course Deployment using Flask APIs, Docker and ElasticSearch

Deployment using Flask APIs, Docker and ElasticSearch

Instructor: Applied AI Course

 

Notes:https://drive.google.com/drive/u/1/folders/1JuLlOiSUxolzHoz0_gtVLHXBqe5vBXxo

 

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Indexing using ElasticSearch
Introduction to A/B Tests

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1.1
How to Learn from Appliedaicourse

35 min
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
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
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
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
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
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
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
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
10.1
Business/Real world problem : Problem definition

6 min
11.1
Business/Real world problem : Overview

13 min
12.1
Business/Real world problem Overview

9 min
12.2
Mapping to ML problem :Fields/Features.

6 min
13.1
Business/Real world problem

10 min
14.1
Business/real world problem :Problem definition

6 min
15.1
Business/Real world problem:Problem definition

6 min
16.1
Code Walkthrough (OpenCV using Python)

17.1
Self Driving Car :Problem definition.

14 min
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

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

21.1
Code Walkthrough: Live session on Basics of Linear Algebra for AI/ML

21.2
Code Walkthrough: Dimensionality Reduction for ML/AI

22.1
Building a simple Youtube recommendation using basic Math

23.1
Interactive Interview Questions(from top product based companies)

23.2
Scenario based interview questions in AI/ML/DataScience

24.1
High-level design of a self driving system

25.1
Machine Learning design: Search engine for Q&A

25.2
ML System Design: Feature Store

31 min
26.1
Sample Interview and Conceptual Questions [AUDIO]

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]