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Applied Machine Learning Online Course
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https://drive.google.com/file/d/1Uz_vwePMhG0ajBEJNocoPaqU__eYCNYu/view
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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]
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