Home Courses Diploma in ML & AI (with UoH) - August -2021 Batch

Diploma in ML & AI (with UoH) - August -2021 Batch

Category: Diploma In AI and ML

Diploma in ML & AI (with UoH) - August -2021 Batch

Category: Diploma In AI and ML

Instructor

AppliedRoots and University of Hyderabad

Preparatory Topics: Foundational Mathematics
Chapters : 3 Assignments : 0 Completed :

How to learn and excel in this program

1.1

Applied Learning with persistence

7 mins

1.2

Learning and Revision Strategy: Feynman’s method + ***Exercise***

10 mins

1.3

LIVE 5th Sept 2021: Welcome and Introduction to the Diploma program.

78 mins

Foundational Math: Co-ordinate Geometry and Linear Algebra

2.1

Introduction

3 mins

2.2

Real world ML classification problem: Sorting Fish in a Factory

11 mins

2.3

Dataset & Plotting

11 mins

2.4

Lines, Circles and Ellipses for ML Classification

16 mins

2.5

Planes, Spheres and Ellipsoids for ML classification

16 mins

2.6

Equation of a line

18 mins

2.7

Equation of a plane & hyper planes.

6 mins

2.8

Equations using Linear Algebra

18 mins

2.9

Dataset using vectors

15 mins

2.10

Dot product and angle between vectors.

10 mins

2.11

Geometry of the Equation of a plane.

22 mins

2.12

Half spaces & classification using a plane.

13 mins

2.13

Distance of a point from a plane

11 mins

2.14

Projection & arithmetic operations on vectors.

12 mins

2.15

Equations of circle, sphere & hypersphere

9 mins

2.16

Classification using hypersphere.

3 mins

2.17

Ellipse, Ellipsoid, Hyper-Ellipsoid

5 mins

2.18

Squares, Rectangles, Hypercubes and Hyper-cuboids

6 mins

2.19

Additional online resources [optional]

1 mins

2.20

Live Session: Coordinate Geometry and Linear Algebra

94 mins

2.21

*** Mandatory Exercise***

60 mins

2.22

LIVE (11th Sept 2021): Linear Algebra and coordinate geometry

106 mins

2.23

Live Session(02nd Oct 2022): Recap of Math for ML

1 mins

Foundational Math: Calculus for ML and AI

3.1

Introduction

3 mins

3.2

Real world problem: Estimate income using purchase data

16 mins

3.3

Visualize the Data

21 mins

3.4

Formulation of regression

23 mins

3.5

Minimisation of multiple values

11 mins

3.6

Limits, Range and Domain of a function

20 mins

3.7

Continuity of functions

6 mins

3.8

Derivatives: geometric intuition

10 mins

3.9

Derivatives: rate of change + Math

13 mins

3.10

Derivative of Common functions

9 mins

3.11

Differentiability of functions

4 mins

3.12

Rules of differentiation

14 mins

3.13

Maxima and Mimina

25 mins

3.14

Examples

11 mins

3.15

Partial derivatives & Del

11 mins

3.16

Optima using Partial derivatives

5 mins

3.17

Saddle point

10 mins

3.18

Gradient Descent

17 mins

3.19

Gradient Descent with multiple variables

5 mins

3.20

Regression using Gradient Descent

20 mins

3.21

Additional Resources [Optional]

1 mins

3.22

***Mandatory Exercise***

60 mins

3.23

Live Session: Calculus for ML

88 mins

3.24

LIVE (18th Sept 2021): Calculus for ML/AI

144 mins

Course 1: Essentials of AI [7 Credits]
Chapters : 16 Assignments : 2 Completed :

Python for Data Science: Introduction

4.1

Python, Anaconda and relevant packages installations

23 mins

4.2

Why learn Python?

4 mins

4.3

Keywords and identifiers

6 mins

4.4

comments, indentation and statements

9 mins

4.5

Variables and data types in Python

32 mins

4.6

Standard Input and Output

7 mins

4.7

Learning Methodology for Programming

6 mins

4.8

Operators

14 mins

4.9

Control flow: if else

10 mins

4.10

Control flow: while loop

16 mins

4.11

Control flow: for loop

15 mins

4.12

Control flow: break and continue

10 mins

4.13

Online tools for Python: Google Colab , HackerRank, LeetCode

14 mins

Inbuilt Data Structures

5.1

Lists

38 mins

5.2

Tuples part 1

10 mins

5.3

Tuples part-2

4 mins

5.4

Sets

16 mins

5.5

Dictionary

21 mins

5.6

Strings

16 mins

Introduction to functions

6.1

Introduction

13 mins

6.2

Types of functions

25 mins

6.3

Function arguments

10 mins

Programming problems-1

7.1

Problem-1: Sum of "n" input integers

18 mins

7.2

Problem-2: Second largest element in a list

18 mins

7.3

Using HackerRank to practice programming

14 mins

7.4

Problem-3: Fizz Buzz

11 mins

7.5

*** Mandatory Exercise***

7.6

Additional practice problems [optional]

7.7

LIVE (25th Sept): Python progamming

117 mins

Insertion Sort

8.1

Sorting & Searching : why bother with these simple tasks?

17 mins

8.2

Satellite data and key

11 mins

8.3

How it works: Card-sorting

20 mins

8.4

Pseudo code

24 mins

8.5

Python Code for Insertion sort

7 mins

8.6

Correctness

6 mins

8.7

In-place sorting

8 mins

8.8

Stable sort

15 mins

8.9

Online sorting

10 mins

Analysing algorithms: Time and Space Complexity

9.1

Model of computation

17 mins

9.2

Space and time analysis of Insertion Sort-1

7 mins

9.3

Space and time analysis of Insertion Sort-2

22 mins

9.4

Insertion sort: Big O-notation

15 mins

9.5

Notations: Big O

15 mins

9.6

Notations: Big Omega, Theta

9 mins

9.7

Notations: Small O, Omega, Theta

24 mins

9.8

Relationships between various notations

21 mins

9.9

Order of common functions & real world applications.

26 mins

9.10

Why does asymptotic analysis matter in the real world?

16 mins

Problems: Big O, Theta, Omega notation

10.1

Solved Problem: Polynomials

18 mins

10.2

Solved Problem: n>n0 case

5 mins

10.3

Solved Problem-1

6 mins

10.4

Solved Problem-2

10 mins

10.5

Solved Problem-3

12 mins

10.6

Solved Problem-4

9 mins

10.7

Solved Problem-5

5 mins

10.8

Solved Problem-6

5 mins

10.9

Solved Problem-7

6 mins

10.10

***Mandatory Exercise***

10.11

LIVE (2nd Oct): Time and Space Complexity

136 mins

Recursion and Dynamic Programming

11.1

Introduction to Recursion

20 mins

11.2

power(a,b) using recursion

25 mins

11.3

Asymptotic Analysis using Recursion Tree method

16 mins

11.4

****Mandatory Programming Exercise: Climbing Stairs*****

11.5

Call-Stack for Recursion

13 mins

11.6

Fibonacci numbers using Recursion

18 mins

11.7

Dynamic Programming (for Fibonacci numbers)

24 mins

11.8

Longest Common Sub-Sequence(LCS) using DP

33 mins

11.9

LCS: Worked out example

21 mins

11.10

Matrix Chain Multiplication using DP

36 mins

11.11

***Mandatory Programming Exercise: Longest Increasing Sub-Sequence***

11.12

Merge Sort-Intuition

17 mins

11.13

MergeSort-PsuedoCode

19 mins

11.14

MergeSort- Why?

6 mins

11.15

Merge sort--Analysing time & space complexity

21 mins

11.16

MergeSort-Recursion Tree method

13 mins

11.17

Linear Search

10 mins

11.18

Merge Sort--External Sorting

25 mins

11.19

Binary Search--intuition

19 mins

11.20

Binary Search-- PseudoCode

9 mins

11.21

Subset-Sum problem (Using DP)

23 mins

11.22

Solved Problem-1

4 mins

11.23

***Mandatory Programming Exercise: Find any Peak element***

11.24

Solved Problem-2

6 mins

11.25

Solved Problem-3

6 mins

11.26

Solved problem-4

8 mins

11.27

Solved problem-5

5 mins

11.28

Solved Problem-6

5 mins

11.29

Solved Problem-7

5 mins

11.30

***Mandatory MCQ Exercise***

11.31

Additional Reading Material [Optional]

11.32

LIVE (9 Oct): Recursion and Dynamic Programming

122 mins

Introduction to Modules/Libraries for DataScience

12.1

What are Modules in Python?

7 mins

12.2

Packages: an introduction

6 mins

12.3

Introduction to NumPy

41 mins

12.4

Common Operations in NumPy

41 mins

12.5

Getting started with Matplotlib

20 mins

12.6

Getting started with Pandas

8 mins

12.7

Understanding DataFrames in Pandas

9 mins

12.8

Common Operations on Data Frames

31 mins

12.9

Downloading a dataset/file to use in Google-Colab

10 mins

Miscellaneous topics in Python

13.1

Lambda functions

8 mins

13.2

File Handling in Python

23 mins

13.3

Exception Handling

15 mins

13.4

Debugging in Python

15 mins

13.5

Solved Problem: Find elements common in two lists

6 mins

13.6

Solved Problem: Find elements common in two lists using a Hashtable/Dict

12 mins

13.7

Build a simple Tiny URL service

24 mins

Object Oriented Programming for ML/AI

14.1

Procedural vs Object Oriented Programming

8 mins

14.2

Classes and Objects ( real-world example: the TinyURL service)

26 mins

14.3

Empty Classes and documentation

4 mins

14.4

Class Variables and Private Members

12 mins

14.5

Abstraction and Inheritance (with real-world examples)

25 mins

14.6

Common questions & experimental learning

14 mins

14.7

Example: OOP for representing Shapes

21 mins

14.8

How is OOP used in an ML role?

7 mins

14.9

Multiple Inheritance

16 mins

14.10

Polymorphism (with real world examples)

19 mins

14.11

Additional topics in OOPs

37 mins

14.12

***Mandatory Programming Exercise- 5 very-easy problems***

14.13

LIVE (16 oct): Internal Working of a Dict and a List in Python

128 mins

Code Walkthroughs & Problem Solving

15.1

Problem -1: Add two randomly generated NumPy matrices using loops [VERY-EASY]

60 mins

15.2

Problem 2: Point inside (or) outside a circle [VERY-EASY]

28 mins

15.3

Problem 3: Resize NumPy n-dim arrays [VERY-EASY]

30 mins

15.4

Problem 4: Find peaks in an array [EASY]

24 mins

15.5

Problem 5: Permutations using recursion and itertools [MEDIUM]

30 mins

15.6

Problem 6: Find x such that sin(x)=cos(x) using Bisection [MEDIUM]

36 mins

15.7

Problem 7: Numerical Integration-I [MEDIUM]

24 mins

15.8

Problem 7 (continued) : Numerical Integration-II [MEDIUM]

34 mins

15.9

Problem 8: Find maxima of a function using code [MEDIUM]

36 mins

15.10

Problem 9: Intersection of circles [VERY-EASY]

8 mins

15.11

Problem 10: First digits of a factorial of a number [EASY]

26 mins

15.12

Problem 11: Estimate the value of PI using Monte Carlo methods [HARD]

31 mins

15.13

*** Mandatory Exercise***

15.14

LIVE (23rd oct): Binary Search Trees & Binary Trees

121 mins

15.15

Code Walkthroughs and Problem Solving(Optional)

1 mins

Regex-- Code Walkthrough & Problem solving

16.1

Introduction to Regular Expressions

14 mins

16.2

Problem 1 : Masking Email

13 mins

16.3

Problem 2: Valid Email or not

18 mins

16.4

Problem 3: Mask Phone numbers

8 mins

16.5

Problem 4: Extract data from PDF invoice

30 mins

16.6

Problem 5: Check if valid integer or float

26 mins

16.7

Problem 6: Regex Matching using Recursion and Dynamic Programming

47 mins

Multiprocessing & Multithreading

17.1

Applied Introduction and Overview

17 mins

17.2

Multiprocessing: Compute the mean of 100 Million numbers

23 mins

17.3

Multi-threading with Code

21 mins

17.4

Joblib: Simplified Parallel Programming in Python

19 mins

Using web-APIs for ML

18.1

Introduction to web-APIs

11 mins

18.2

Speech to text (Code using Google's API)

38 mins

18.3

Bing Search (Web and Image) APIs

37 mins

18.4

Live (30Oct): Item-Item Collaborative Filtering for Recommender Systems

144 mins

18.5

How to code effectively and build a web-scraper

1 mins

18.6

Using Web-APIs in Python for Machine Learning

1 mins

SQL

19.1

Introduction to Databases

22 mins

19.2

Why SQL?

15 mins

19.3

Execution of an SQL statement.

7 mins

19.4

IMDB dataset

12 mins

19.5

Installing MySQL

11 mins

19.6

Load IMDB data.

4 mins

19.7

USE, DESCRIBE, SHOW TABLES

15 mins

19.8

SELECT

20 mins

19.9

LIMIT, OFFSET

10 mins

19.10

ORDER BY

6 mins

19.11

DISTINCT

10 mins

19.12

WHERE, Comparison operators, NULL

13 mins

19.13

Logical Operators

27 mins

19.14

Aggregate Functions: COUNT, MIN, MAX, AVG, SUM

8 mins

19.15

GROUP BY

13 mins

19.16

HAVING

12 mins

19.17

Order of keywords.

4 mins

19.18

Join and Natural Join

12 mins

19.19

Inner, Left, Right and Outer joins.

23 mins

19.20

Sub Queries/Nested Queries/Inner Queries

24 mins

19.21

DML:INSERT

7 mins

19.22

DML:UPDATE , DELETE

6 mins

19.23

DDL:CREATE TABLE

12 mins

19.24

DDL:ALTER: ADD, MODIFY, DROP

4 mins

19.25

DDL:DROP TABLE, TRUNCATE, DELETE

3 mins

19.26

Data Control Language: GRANT, REVOKE

10 mins

19.27

Learning resources

3 mins

19.28

Solved Problems: 1-5

25 mins

19.29

Solved Problems: 6-10

13 mins

19.30

Solved Problems: 11-14

14 mins

19.31

***Mandatory Assignment: 6 MSQs***

19.32

[Optional] Additional Practice problems

19.33

[Optional] MongoDB for Documents (NoSQL)- Part 1

1 mins

19.34

[Optional] MongoDB for Documents (NoSQL)- Part 2

1 mins

19.35

LIVE (6th Nov) How to take the mid term exams

101 mins

19.36

Live Session(22nd January 2023): Interview Session for ML Engineer roles

1 mins

Course 2 : Data Analysis and Visualisation [7 Credits]
Chapters : 9 Assignments : 2 Completed :

Plotting for exploratory data analysis (EDA)

20.1

Introduction to IRIS dataset and 2D scatter plot

26 mins

20.2

3D scatter plot

6 mins

20.3

Pair plots

14 mins

20.4

Limitations of Pair Plots

2 mins

20.5

Histogram and Introduction to PDF(Probability Density Function)

17 mins

20.6

Univariate Analysis using the density function

6 mins

20.7

CDF(Cumulative Distribution Function)

15 mins

20.8

Mean, Variance and Standard Deviation

17 mins

20.9

Median

10 mins

20.10

Percentiles and Quantiles

9 mins

20.11

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

6 mins

20.12

Box-plot with Whiskers

9 mins

20.13

Violin Plots

4 mins

20.14

Summarizing Plots, Univariate, Bivariate and Multivariate analysis

6 mins

20.15

Multivariate Probability Density, Contour Plot

9 mins

20.16

***Mandatory Coding Exercise*******

60 mins

Probability Theory

21.1

How we learn probability theory?

2 mins

21.2

Introduction to Probability and Statistics

17 mins

21.3

Population and Sample

7 mins

21.4

Probability from a set theoretic view

23 mins

21.5

Axioms of Probability, Properties and Examples

54 mins

21.6

Conditional Probability & Examples

22 mins

21.7

Multiplication theorem

36 mins

21.8

Independent events

26 mins

21.9

Law of total Probability

14 mins

21.10

Baye's Theorem

30 mins

21.11

Solved problems

15 mins

21.12

Random variables with examples

25 mins

21.13

PMF, CDF and PDF of random variables

43 mins

21.14

Expectation

37 mins

21.15

Probability Distributions: Bernoulli and Binomial

48 mins

21.16

Poisson Distribution

24 mins

21.17

Uniform (continuous) distribution

15 mins

21.18

Exponential Distribution

22 mins

21.19

Normal/gaussian Distribution

19 mins

21.20

How distributions are used?

17 mins

21.21

53 Practice Questions and answers

1 mins

21.22

Live session: Exploratory Data Analysis

1 mins

21.23

Q&A on Probability and Statistics

1 mins

21.24

LIVE_ Interview Questions on Probability & Stats

1 mins

21.25

Live Session(16th Oct 2022): Recap of Probability and Statistics

1 mins

Applied Probability & Stats--Concepts

22.1

CDF of Gaussian/Normal Distribution

11 mins

22.2

Symmetric distribution, Skewness and Kurtosis

25 mins

22.3

Standard normal variate (Z) and standardization

6 mins

22.4

Kernel density estimation (KDE)

7 mins

22.5

Sampling distribution & Central Limit theorem (CLT)

19 mins

22.6

Q-Q plot: How to test if a random variable is normally distributed or not?

23 mins

22.7

Chebyshev’s inequality

20 mins

22.8

Chebyshev's Inequality: Question related to the Mathematical derivation

8 mins

22.9

Discrete and Continuous Uniform distributions

13 mins

22.10

How to randomly sample data points (Uniform Distribution)

10 mins

22.11

Log Normal Distribution

12 mins

22.12

Power law distributions

12 mins

22.13

Box cox transform

12 mins

22.14

Applications of non-gaussian distributions?

26 mins

22.15

Co-variance

14 mins

22.16

Pearson Correlation Coefficient

13 mins

22.17

Spearman Rank Correlation Coefficient

7 mins

22.18

Correlation vs Causation

5 mins

22.19

How to use correlations?

13 mins

22.20

Confidence interval (C.I): An Introduction

8 mins

22.21

Computing confidence interval given the underlying distribution

11 mins

22.22

C.I for mean of a random variable

14 mins

22.23

Confidence interval using bootstrapping

18 mins

22.24

Hypothesis testing methodology, Null-hypothesis, p-value

16 mins

22.25

Hypothesis Testing Intution with coin toss example

27 mins

22.26

Resampling and permutation test

15 mins

22.27

Common confusions about Hypothesis testing and Permutation testing

28 mins

22.28

K-S Test for similarity of two distributions

15 mins

22.29

p-value for KS-test

8 mins

22.30

Code Snippet K-S Test

5 mins

22.31

Hypothesis testing: another example

18 mins

22.32

How to use hypothesis testing?

23 mins

22.33

Live Session(11th December 2021): Applied Probability & Stats--Concepts

78 mins

22.34

Live session (25th December 2021):Probability & Stats with Simulations by Prof CR Rao

128 mins

22.35

Proportional Sampling

1 mins

Code Walkthroughs and Simulations

23.1

Simulation: Central Limit theorem

32 mins

23.2

Simulation: Confidence intervals

35 mins

23.3

Simulation: Hypothesis testing

22 mins

23.4

Code Walkthrough: QQ-Plot and Box-Cox transform

38 mins

23.5

Simulation: Bootstrapping

37 mins

23.6

Measuring Causality using A/B tests

30 mins

23.7

Live Session (1st Jan 2023): Interview Session for Data Analyst roles

1 mins

Optional Videos in Data Analysis [Tableau/Interview Questions/Practical Applications]

24.1

Which plotting technique to use? [with examples]

124 mins

24.2

Interview Questions [from various companies]

105 mins

24.3

Tableau-1

109 mins

24.4

Tableau-2

79 mins

Dimensionality reduction -- Foundations

25.1

What is Dimensionality reduction?

3 mins

25.2

Row Vector and Column Vector

5 mins

25.3

How to represent a data set?

4 mins

25.4

How to represent a dataset as a Matrix.

7 mins

25.5

Data Preprocessing: Feature Normalisation

20 mins

25.6

Mean of a data matrix

6 mins

25.7

Data Preprocessing: Column Standardization

16 mins

25.8

Co-variance of a Data Matrix

24 mins

25.9

MNIST dataset (784 dimensional)

20 mins

25.10

Code to Load MNIST Data Set

12 mins

PCA (Principal Component Analysis)

26.1

Why learn PCA?

4 mins

26.2

Geometric intuition of PCA

14 mins

26.3

Mathematical objective function of PCA

13 mins

26.4

Alternative formulation of PCA: Distance minimization

10 mins

26.5

Eigen values and Eigen vectors (PCA): Dimensionality reduction

23 mins

26.6

PCA for Dimensionality Reduction and Visualization

10 mins

26.7

Visualize MNIST dataset

5 mins

26.8

Limitations of PCA

5 mins

26.9

PCA Code example

19 mins

26.10

PCA for dimensionality reduction (not-visualization)

15 mins

(t-SNE) embedding

27.1

What is t-SNE?

7 mins

27.2

Neighborhood of a point, Embedding

7 mins

27.3

Geometric intuition of t-SNE

9 mins

27.4

Crowding Problem

8 mins

27.5

How to apply t-SNE and interpret its output

38 mins

27.6

t-SNE on MNIST

7 mins

27.7

Code example of t-SNE

9 mins

27.8

Revision Questions

30 mins

27.9

51 Practice Questions and Answers

1 mins

Interview Questions on Dimensionality Reduction

28.1

Questions & Answers

30 mins

Course 3 : Machine Learning [7 Credits]
Chapters : 17 Assignments : 3 Completed :

Real world problem: Predict rating given product reviews on Amazon

29.1

Dataset overview: Amazon Fine Food reviews(EDA)

23 mins

29.2

Data Cleaning: Deduplication

15 mins

29.3

Why convert text to a vector?

14 mins

29.4

Bag of Words (BoW)

18 mins

29.5

Text Preprocessing: Stemming, Stop-word removal, Tokenization, Lemmatization.

15 mins

29.6

uni-gram, bi-gram, n-grams.

9 mins

29.7

tf-idf (term frequency- inverse document frequency)

22 mins

29.8

Why use log in IDF?

14 mins

29.9

Word2Vec.

16 mins

29.10

Avg-Word2Vec, tf-idf weighted Word2Vec

9 mins

29.11

Bag of Words( Code Sample)

19 mins

29.12

Text Preprocessing( Code Sample)

11 mins

29.13

Bi-Grams and n-grams (Code Sample)

5 mins

29.14

TF-IDF (Code Sample)

6 mins

29.15

Word2Vec (Code Sample)

12 mins

29.16

Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)

2 mins

29.17

****Mandatory Assignment: Implement TF-IDF in a simplified form from scratch

1 mins

Classification And Regression Models: K-Nearest Neighbors

30.1

How “Classification” works?

10 mins

30.2

Data matrix notation

7 mins

30.3

Classification vs Regression (examples)

6 mins

30.4

K-Nearest Neighbours Geometric intuition with a toy example

12 mins

30.5

Failure cases of KNN

7 mins

30.6

Distance measures: Euclidean(L2) , Manhattan(L1), Minkowski, Hamming

20 mins

30.7

Cosine Distance & Cosine Similarity

19 mins

30.8

How to measure the effectiveness of k-NN?

16 mins

30.9

Test/Evaluation time and space complexity

12 mins

30.10

KNN Limitations

9 mins

30.11

Decision surface for K-NN as K changes

23 mins

30.12

Overfitting and Underfitting

12 mins

30.13

Need for Cross validation

22 mins

30.14

K-fold cross validation

18 mins

30.15

Visualizing train, validation and test datasets

13 mins

30.16

How to determine overfitting and underfitting?

19 mins

30.17

Time based splitting

19 mins

30.18

k-NN for regression

5 mins

30.19

Weighted k-NN

8 mins

30.20

Voronoi diagram

4 mins

30.21

Binary search tree

16 mins

30.22

How to build a kd-tree

17 mins

30.23

Find nearest neighbours using kd-tree

13 mins

30.24

Limitations of Kd tree

9 mins

30.25

Extensions

3 mins

30.26

Hashing vs LSH

10 mins

30.27

LSH for cosine similarity

40 mins

30.28

LSH for euclidean distance

13 mins

30.29

Probabilistic class label

8 mins

30.30

Code Sample:Decision boundary .

23 mins

30.31

Code Sample:Cross Validation

13 mins

30.32

Revision/Interview Questions

30 mins

30.33

*******Mandatory Assignment: Implement KNN with LSH from scratch*********

1 mins

Performance measurement of models

31.1

Accuracy

15 mins

31.2

Confusion matrix, TPR, FPR, FNR, TNR

25 mins

31.3

Precision and recall, F1-score

10 mins

31.4

Receiver Operating Characteristic Curve (ROC) curve and AUC

19 mins

31.5

Log-loss

12 mins

31.6

R-Squared/Coefficient of determination

14 mins

31.7

Median absolute deviation (MAD)

5 mins

31.8

Distribution of errors

7 mins

31.9

Revision Questions

30 mins

Classification algorithms in various situations