Question 1

Statistics

Accepted Answer

Data and its properties
 	Basic Statistics
 	Univariate analysis
 	Bivariate analysis
 	Statistical tests

 	Hypothesis test
 	t-Test
 	Chi-Square test
 	ANOVA

Question 2

Machine Learning

Accepted Answer

Regression analysis

 	Introduction to regression analysis
 	Different regression analysis techniques


 	Classification

 	Introduction to classification
 	Different classification techniques


 	Clustering

 	Introduction to clustering
 	Different clustering techniques

Question 3

Recommender Systems

Accepted Answer

Overview
 	Content-based filtering
 	Collaborative filtering

Question 4

Association Rule Learning

Accepted Answer

Apriori
 	Eclat
 	FP-Growth

Question 5

Time Series Analysis

Accepted Answer

Moving Average
 	ARIMA
 	SARIMA

Question 6

Deep Learning

Accepted Answer

Gradient Descent and Optimization
 	Introduction to Neural Networks
 	Convolutional Neural Network
 	Recurrent Neural Networks
 	Encoder-Decoder Models
 	LSTM Model
 	Generative Adversarial Network (GAN)

Question 7

Computer Vision

Accepted Answer

Image Processing
 	Image Classification
 	Object Detection

Question 8

Natural Language Processing

Accepted Answer

Text Analysis
 	Text Classification
 	Sentiment Analysis
 	Word2Vec Models
 	Sequence Modelling

Question 9

Capstone Project

Accepted Answer

A complete project that covers the skills earned during the course with guidance from industry mentors and support from learner success managers throughout.

Question 10

Who can attend this course?

Accepted Answer

This course is suitable for beginners as well as professionals working in different domains.

Question 11

What is the mode of delivery?

Accepted Answer

This course has different modes of delivery. It can be taken in person from a center over the weeks, or it can be taken online as well based on your own convenience.

Question 12

Will there be an instructor also in this course?

Accepted Answer

Yes, an instructor will be there for in-person delivery and online (instructor-led) delivery.

Question 13

What are the prerequisites to attend this course?

Accepted Answer

Basic understanding of mathematics, python, and data structure is required to attend this course.

Question 14

How difficult is this course to learn?

Accepted Answer

The course has intermediate-level difficulty.

Question 15

What job roles can i take if i attend this course?

Accepted Answer

Data scientist, machine learning engineer, AI engineer, data analyst, computer vision engineer, NLP engineer, etc. roles can be explored after taking this course.

Question 16

Who can get the certificate?

Accepted Answer

All attendees who complete all sections of the course along with the capstone project will get the certificate.

Question 17

How the completeness of each section is measured in the course?

Accepted Answer

There are MCQ-based short tests given after each section to assess the understanding of a learner in each section. Finally, the capstone project is evaluated manually by the instructors.

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