Machine Learning & AI 2021by upGrad
Certificate Course: Machine Learning & Artificial Intelligence (ML_AI)
Collaboration with: upGrad
Duration: January 2021 – August 2021

Students who have successfully completed the course: 23

1

DEV SHARMA

B.Tech

CSE with Data Science

2

GURJOT SINGH

B.Tech

CSE with Data Science

3

HARSH SHARMA

B.Tech

CSE with Data Science

4

PANKAJ JHA

B.Tech

CSE with Data Science

5

PRANJAL KUMAR

PGD

Data Science

6

RAHUL SINGH

B.Tech

CSE with Data Science

7

SHUBHAM PRADHAN

B.Tech

CSE with Data Science

8

AANCHAL KAMBOJ

B.Tech

CSE with Data Science

9

ANKU KUMARI

PGD

Data Science

10

DEVANSH SHARMA

B.Tech

CSE with Data Science

11

DOUZI MUSADDIQ KHAN

B.Tech

CSE with Data Science

12

KUNAL BAGCHI

B.Tech

CSE with Data Science

13

MANISH KUMAR

B.Tech

CSE with Data Science

14

MD FAHAD MOHBOOB

B.Tech

CSE with Data Science

15

NIKHIL GULATI

B.Tech

CSE with Data Science

16

RITWIK SINHA

B.Tech

CSE with Data Science

17

SADIVA MADAN

B.Tech

CSE with Data Science

18

SAGAR KISHAN VERMA

MSc

Data Science

19

SOUMYA RANJAN MISHRA

B.Tech

CSE with Data Science

20

TARUN SHARMA

B.Tech

CSE with Data Science

21

VARUN KUTTAN

MSc

Data Science

22

VIKASH VERMA

B.Tech

CSE with Data Science

23

VISHNU KANT SHUKLA

B.Tech

CSE with Data Science

Placement:

Student

Company

Ritwik Sinha

Merkle Sokrati

Varun Kuttan

Investosure

Dev Sharma

Xceedance

Harsh Sharma

Investosure

Syllabus:

Module

Session Title

Data Analysis in Excel

Understanding Excel Interface.
Basic formatting
Conditional Formatting
Logical, call references and text functions

Compound Functions and Relational Operators
Pattern Matching with Wildcards
Basics of Sorting

VLOOKUP and HLOOKUP
Pivot Tables
Analysing Large Datasets

Data Analysis using SQL

An introduction to RDBMS and SQL
Basics of SQL
Data Retrieval with SQL

Compound Functions and Relational Operators
Pattern Matching with Wildcards
Basics of Sorting

ORDER BY, GROUP BY and HAVING clause
Aggregate Functions
Joins

Nested Queries
Reading Delimited and Relational Databases

Advanced SQL[Videos (*Optional) + Graded Assessments]

Database Design

Updating Table
Widow functions

User-defined Functions

Query Optimisation

My SQL Coding Practice

MySQL Coding Pratice Week

Intro to Python

Understanding UpGrad Coding Console
Installation and Introduction to Jupyter notebook
Basics

Lists
Tuples
Dictionaries
Sets

Control Structures
Functions

Map, Reduce, Filter

NumPy – Python for DS

Introduction to Python Libraries
Introduction to Numpy
Numpy Basics

Operations on NumPy Arrays

Coding Question

Pandas – Python for DS

Introduction to Pandas

Indexing and Selection Data
Merge and Append

Grouping and Summarizing Dataframes
Lambda Function and Pivot tables.

Revise Lambda function
Coding questions on pandas

Data Cleaning using Python + Overview Kaggle

Fixing Rows and Columns
Missing Values

Standardising Values
Invalid Values
Filtering Data

Cleaning Datasets

Kaggle Platform overview

Visualisation in Python

Introduction to Data Visualisation

Basics of Visualisation

Plotting Data Distributions

Plotting Categorical and Time-Series Data

Tableau

Introduction to Tableau

Data Preparation & Hierarchies in Tableau

Visualising and Analysing Data in Tableau

Exploratory Data Analysis (EDA)

Introdution to EDA
Data Sourcing
Data Cleaning

Univariate Analysis

Segmented Univariate Analysis

Bivariate Analysis

Derived Metrics

EDA Case Study & Uber

 

Inferential Statistics

Basics of Probability

Discrete Probability Distributions

Continuous Probability Distributions

Inferential Statistics

Continuous Probability Distributions

Central Limit Theorem

Hypothesis Testing

Concepts of Hypothesis Testing

Overview of ML algorithms-Linear regression

Linear Regression

Logistic Regression

Project – Prediction of Car Prices

 

ML algorithms- Logistic regression + Clustering

Logistic Regression

Clustering

Dummy Project Walkthrough – Apparent Temperature Prediction project

 

Regression Project – Demo

UnSupervised Learning – Clustering

 
 

Introduction to NLP

What is NLP
 NLP: Areas of Application
 Understanding Text
 Text Encoding
 Regular expressions: Quantifiers – I

Comprehension: Regular Expressions
 Regular Expressions: Anchors and Wildcard

Regular Expressions: Characters Sets
 Greedy versus Non-greedy Search
 Commonly Used RE Functions

Regular Expressions: Grouping
 Regular Expressions: Use Cases

Basic Lexical Processing

Word Frequencies and Stop Words
 Tokenisation
 Bag-of-Words Representation

Stemming and Lemmatization
 Final Bag-of-Words Representation

Advanced Lexical processing

TF-IDF Representation
 Introduction to Advanced lexical processing
 Canonicalisation

Phonetic Hashing
 Edit Distance

Spell Corrector
 Pointwise Mutual Information

Clear doubts based on query, modules taught and quizzes.

Project – Bank Marketing

 

Bayes’ Theorem and Its Building Blocks

Introduction to Naive Bayes
 Conditional Probability and Its Intuition
 Bayes’ Theorem
 Naive Bayes -With One Feature

Naive Bayes For Categorical Data

Conditional Independence in Naive Bayes
 Deciphering Naive Bayes with an example data set
 Introduction – Naive Bayes for Text Classification
 Document Classifier – Pre Processing Steps
 Document Classifier – Worked out Example

Naive Bayes For Categorical Data

Laplace Smoothing
 Quick Introduction to types of Naive Bayes – Multinomial, Bernoulli

Naive Bayes for Text Classification

Python hands on:
 Building a Naive Bayes classifier : Multinomial
 Building a Naive Bayes classifier : Bernoulli