Home Courses AD-Click Predicition

AD-Click Predicition

Category: AI & Machine Learning

AD-Click Predicition

Category: AI & Machine Learning

For our Instructor led programs in Data Science & ML

Module 1: Fundamentals of Programming
Chapters : 7 Assignments : 0 Completed :

Python for Data Science Introduction

1.1

Python, Anaconda and relevant packages installations

23 mins

1.2

Why learn Python?

4 mins

1.3

Keywords and identifiers

6 mins

1.4

comments, indentation and statements

9 mins

1.5

Variables and data types in Python

32 mins

1.6

Standard Input and Output

7 mins

1.7

Operators

14 mins

1.8

Control flow: if else

10 mins

1.9

Control flow: while loop

16 mins

1.10

Control flow: for loop

15 mins

1.11

Control flow: break and continue

10 mins

Python for Data Science: Data Structures

2.1

Lists

38 mins

2.2

Tuples part-1

10 mins

2.3

Tuples part-2

4 mins

2.4

Sets

16 mins

2.5

Dictionary

21 mins

2.6

Strings

16 mins

Python for Data Science: Functions

3.1

Introduction

13 mins

3.2

Function arguments

10 mins

3.3

Recursive functions

16 mins

3.4

Lambda functions

8 mins

3.5

Modules

7 mins

3.6

Types of functions

25 mins

3.7

Packages

6 mins

3.8

File Handling

23 mins

3.9

Exception Handling

15 mins

3.10

Debugging Python

15 mins

Python for Data Science: Numpy

4.1

Numpy Introduction

41 mins

4.2

Numerical operations on Numpy

41 mins

Python for Data Science: Pandas

5.1

Getting started with pandas

8 mins

5.2

Data Frame Basics

9 mins

5.3

Key Operations on Data Frames

31 mins

Python for Data Science: Matplotlib

6.1

Getting started with Matplotlib

20 mins

Python for Data Science: Computational Complexity

7.1

Space and Time Complexity: Find largest number in a list

20 mins

7.2

Binary search

17 mins

7.3

Find elements common in two lists

6 mins

7.4

Find elements common in two lists using a Hash table/Dict

12 mins

Module 2: Data Science: Exploratory Data Analysis and Data Visualization
Chapters : 6 Assignments : 0 Completed :

Plotting for exploratory data analysis (EDA)

8.1

Introduction to IRIS dataset and 2D scatter plot

26 mins

8.2

3D scatter plot

6 mins

8.3

Pair plots

14 mins

8.4

Limitations of pair plots

2 mins

8.5

Histogram and Introduction to PDF(Probability Density Function)

17 mins

8.6

Univariate Analysis using PDF

6 mins

8.7

CDF(Cumulative Distribution Function)

8.8

Mean, Variance and Standard Deviation

17 mins

8.9

Median

10 mins

8.10

Percentiles and Quantiles

9 mins

8.11

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

30 mins

8.12

Box-plot with Whiskers

9 mins

8.13

Violin Plots

4 mins

8.14

Summarizing Plots, Univariate, Bivariate and Multivariate analysis

6 mins

8.15

Multivariate Probability Density, Contour Plot

9 mins

8.16

Exercise: Perform EDA on Haberman dataset

4 mins

Linear Algebra

9.1

Why learn it ?

4 mins

9.2

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

14 mins

9.3

Dot Product and Angle between 2 Vectors

14 mins

9.4

Projection and Unit Vector

5 mins

9.5

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

23 mins

9.6

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

10 mins

9.7

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

7 mins

9.8

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

6 mins

9.9

Square ,Rectangle

6 mins

9.10

Hyper Cube,Hyper Cuboid

3 mins

9.11

Assignment[Optional]

30 mins

9.12

Probability and Statistics

10.1

Introduction to Probability and Statistics

17 mins

10.2

Population and Sample

7 mins

10.3

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

27 mins

10.4

CDF(Cumulative Distribution function) of Gaussian/Normal distribution

11 mins

10.5

Symmetric distribution, Skewness and Kurtosis

5 mins

10.6

Standard normal variate (z) and standardization

15 mins

10.7

Kernel density estimation

7 mins

10.8

Sampling distribution & Central Limit theorem

19 mins

10.9

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

23 mins

10.10

Discrete and Continuous Uniform distributions

13 mins

10.11

How to randomly sample data points (Uniform Distribution)

10 mins

10.12

Log Normal Distribution

12 mins

10.13

Power law distribution

12 mins

10.14

Box cox transform

12 mins

10.15

Co-variance

14 mins

10.16

Pearson Correlation Coefficient

13 mins

10.17

Spearman Rank Correlation Coefficient

7 mins

10.18

Correlation vs Causation

3 mins

10.19

Confidence interval (C.I) Introduction

8 mins

10.20

Computing confidence-interval given distribution

11 mins

10.21

C.I for mean of a normal random variable

14 mins

10.22

Confidence interval using bootstrapping

17 mins

10.23

Hypothesis testing methodology, Null-hypothesis, p-value

16 mins

10.24

Resampling and permutation test

15 mins

10.25

K-S Test

6 mins

10.26

K-S Test for similarity of two distributions

15 mins

10.27

Bernoulli and Binomial Distribution

11 mins

10.28

Code Snippet K-S Test

6 mins

10.29

Hypothesis Testing Intution with coin toss example

27 mins

10.30

Hypothesis testing Mean differences Example

18 mins

10.31

Resampling and Permutation test for Mean difference example

19 mins

Dimensionality reduction and Visualization:

11.1

what is dimensionality reduction?

3 mins

11.2

Row vector, Column vector: Iris dataset example

5 mins

11.3

Represent a dataset: D= {x_i, y_i}

5 mins

11.4

Represent a dataset as a Matrix

7 mins

11.5

Data preprocessing: Column Normalization

20 mins

11.6

Mean of a data matrix

6 mins

11.7

Data preprocessing: Column Standardization

6 mins

11.8

Co-variance of a Data Matrix

24 mins

11.9

MNIST dataset (784 dimensional)

20 mins

11.10

Code to load MNIST Dataset

12 mins

PCA(principal component analysis)

12.1

Why learn it

4 mins

12.2

Geometric intuition

14 mins

12.3

Mathematical objective function

13 mins

12.4

Alternative formulation of PCA: distance minimization

10 mins

12.5

Eigenvalues and eigenvectors

23 mins

12.6

PCA for dimensionality reduction and visualization

10 mins

12.7

Visualize MNIST dataset

5 mins

12.8

Limitations of PCA

5 mins

12.9

Code example using visualization

19 mins

12.10

PCA for dimensionality reduction (not-visualization)

15 mins

(t-SNE)T-distributed Stochastic Neighbourhood Embedding

13.1

What is t-SNE?

7 mins

13.2

Neighborhood of a point, Embedding

7 mins

13.3

Geometric intuition

9 mins

13.4

Crowding Problem

8 mins

13.5

How to apply t-SNE and interpret its output

38 mins

13.6

t-SNE on MNIST

7 mins

13.7

Code example

9 mins

Module 3: Foundations of Natural Language Processing and Machine Learning
Chapters : 6 Assignments : 0 Completed :

Real world problem: Predict rating given product reviews on Amazon

14.1

Dataset overview: Amazon Fine Food reviews(EDA)

23 mins

14.2

Data Cleaning: Deduplication

15 mins

14.3

Why convert text to a vector?

14 mins

14.4

Bag of Words (BoW)

18 mins

14.5

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

15 mins

14.6

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

9 mins

14.7

tf-idf (term frequency- inverse document frequency)

22 mins

14.8

Why use log in IDF?

14 mins

14.9

Word2Vec.

16 mins

14.10

Avg-Word2Vec, tf-idf weighted Word2Vec

9 mins

14.11

Bag of Words( Code Sample)

19 mins

14.12

Text Preprocessing( Code Sample)

11 mins

14.13

Bi-Grams and n-grams (Code Sample)

5 mins

14.14

TF-IDF (Code Sample)

6 mins

14.15

Word2Vec (Code Sample)

12 mins

14.16

Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)

2 mins

14.17

Exercise: t-SNE visualization of Amazon reviews with polarity based color-coding

6 mins

Classification And Regression Models: K-Nearest Neighbours

15.1

How “Classification” works?

10 mins

15.2

Data matrix notation

7 mins

15.3

Classification vs Regression (examples)

6 mins

15.4

K-Nearest Neighbours Geometric intuition with a toy example

11 mins

15.5

Failure cases of KNN

7 mins

15.6

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

20 mins

15.7

Cosine Distance & Cosine Similarity

19 mins

15.8

How to measure the effectiveness of k-NN?

16 mins

15.9

Test/Evaluation time and space complexity

12 mins

15.10

KNN Limitations

15.11

Decision surface for K-NN as K changes

23 mins

15.12

Overfitting and Underfitting

12 mins

15.13

Need for Cross validation

22 mins

15.14

K-fold cross validation

17 mins

15.15

Visualizing train, validation and test datasets

13 mins

15.16

How to determine overfitting and underfitting?

19 mins

15.17

Time based splitting

19 mins

15.18

k-NN for regression

5 mins

15.19

Weighted k-NN

8 mins

15.20

Voronoi diagram

4 mins

15.21

Binary search tree

16 mins

15.22

How to build a kd-tree

17 mins

15.23

Find nearest neighbours using kd-tree

13 mins

15.24

Limitations of Kd tree

9 mins

15.25

Extensions

3 mins

15.26

Hashing vs LSH

10 mins

15.27

LSH for cosine similarity

40 mins

15.28

LSH for euclidean distance

13 mins

15.29

Probabilistic class label

8 mins

15.30

Code Sample:Decision boundary .

23 mins

15.31

Code Sample:Cross Validation

13 mins

15.32

Exercise: Apply k-NN on Amazon reviews dataset

5 mins

15.33

Revision Questions

30 mins

Performance measurement of models

16.1

Accuracy

15 mins

16.2

Confusion matrix, TPR, FPR, FNR, TNR

25 mins

16.3

Distribution of errors

7 mins

16.4

Receiver Operating Characteristic Curve (ROC) curve and AUC

19 mins

16.5

Log-loss

12 mins

16.6

R-Squared/Coefficient of determination

14 mins

16.7

Median absolute deviation (MAD)

5 mins

16.8

Revision Questions

30 mins

16.9

Precision and recall, F1-score

10 mins

Logistic Regression

17.1

Geometric intuition of Logistic Regression

31 mins

17.2

Sigmoid function: Squashing

37 mins

17.3

Mathematical formulation of Objective function

24 mins

17.4

Weight vector

11 mins

17.5

L2 Regularization: Overfitting and Underfitting

26 mins

17.6

L1 regularization and sparsity

11 mins

17.7

Probabilistic Interpretation: Gaussian Naive Bayes

19 mins

17.8

Loss minimization interpretation

24 mins

17.9

hyperparameters and random search

16 mins

17.10

Column Standardization

5 mins

17.11

Feature importance and Model interpretability

14 mins

17.12

Collinearity of features

14 mins

17.13

Test/Run time space and time complexity

10 mins

17.14

Real world cases

11 mins

17.15

Non-linearly separable data & feature engineering

28 mins

17.16

Code sample: Logistic regression, GridSearchCV, RandomSearchCV

23 mins

17.17

Exercise: Apply Logistic regression to Amazon reviews dataset.

6 mins

17.18

Extensions to Generalized linear models

9 mins

Linear Regression

18.1

Geometric intuition of Linear Regression

13 mins

18.2

Mathematical formulation

14 mins

18.3

Real world Cases

8 mins

18.4

Code sample for Linear Regression

13 mins

Solving optimization problems

19.1

Differentiation

29 mins

19.2

Revision Questions

30 mins

19.3

Online differentiation tools

8 mins

19.4

Maxima and Minima

12 mins

19.5

Vector calculus: Grad

10 mins

19.6

Gradient descent: geometric intuition

19 mins

19.7

Learning rate

8 mins

19.8

Gradient descent for linear regression

8 mins

19.9

SGD algorithm

9 mins

19.10

Constrained Optimization & PCA

14 mins

19.11

Logistic regression formulation revisited

6 mins

19.12

Why L1 regularization creates sparsity?

17 mins

19.13

Exercise: Implement SGD for linear regression

6 mins

19.14

Revision questions

30 mins

Module 4: Machine Learning -II (supervised Learning Models)
Chapters : 1 Assignments : 0 Completed :

Ensemble Models

20.1

What are ensembles?

6 mins

20.2

Bootstrapped Aggregation (Bagging) Intuition

17 mins

20.3

Random Forest and their construction

15 mins

20.4

Bias-Variance tradeoff

7 mins

20.5

Train and run time complexity

20.6

Bagging:Code Sample

4 mins

20.7

Extremely randomized trees

8 mins

20.8

Random Tree :Cases

6 mins

20.9

Boosting Intuition

17 mins

20.10

Residuals, Loss functions and gradients

13 mins

20.11

Gradient Boosting

10 mins

20.12

Regularization by Shrinkage

8 mins

20.13

Train and Run time complexity

6 mins

20.14

XGBoost: Boosting + Randomization

14 mins

20.15

AdaBoost: geometric intuition

7 mins

20.16

Stacking models

22 mins

20.17

Cascading classifiers

15 mins

20.18

Kaggle competitions vs Real world

9 mins

20.19

Exercise: Apply GBDT and RF to Amazon reviews dataset.

4 mins

20.20

Revision Questions

30 mins

Ad- Click Prediction Case Study Explanation
Chapters : 1 Assignments : 0 Completed :

Ad Click Prediction Case Study explanation

21.1

Case Study Explanation

138 mins

21.2

Case Study Explanation (contd.) and Performance metrics

131 mins

Statement:

A key technology behind search advertising is to predict the click-through rate (pCTR) of ads, as the economic model behind search advertising requires pCTR values to rank ads and to price clicks. Given the training instances derived from session logs of the Tencent proprietary search engine, soso.com, participants are expected to accurately predict the pCTR of ads in the testing instances.

Data Type
1. csv files
2. Both text and integer values
3. Training Data
  - QueryId_tokensid.txt (id query_toks1|query_toks2|query_toks3|...)
  - PurchaseId_tokensid.txt (id Purchase_toks1|Purchase_toks2|...)
  - TitleId_tokensid.txt (id Title_toks1|Title_toks2|Title_toks3|...)
  - DescriptionId_tokensid.txt (id Description_toks1|Description_toks2|...)
  - UserId_tokensid.txt (UserId, gender{0,1,2}, age)
4. Test data set The testing dataset shares the same format as the training dataset, except for the counts of ad impressions and ad clicks that are needed for computing the empirical CTR.
Data Size: 700 MB

Key Points:

Validity of this course is 240 days( i.e Starts from the date of your registration to this course)
Expert Guidance, we will try to answer your queries in atmost 24hours
10+ machine learning algorithms will be taught in this course.
No prerequisites-- we will teach every thing from basics ( we just expect you to know basic programming)
Python for Data science is part of the course curriculum.

Target Audience:

We are building our course content and teaching methodology to cater to the needs to students at various levels of expertise and varying background skills. This course can be taken by anyone with a working knowledge of a modern programming language like C/C++/Java/Python. We expect the average student to spend at least 5 hours a week over a 6 month period amounting to a 145+ hours of effort. More the effort, better the results. Here is a list of customers who would benefit from our course:

1. Undergrad (BS/BTech/BE) students in engineering and science.
2. Grad(MS/MTech/ME/MCA) students in engineering and science.
3. Working professionals: Software engineers, Business analysts, Product managers, Program managers, Managers, Startup teams building ML products/services.