1: Introduction

What is Python..?

A Brief history of Python

Why Should I learn Python..?

Installing Python

How to execute Python program

Write your first program

2: Variables & Data Types

Variables

Numbers

String

Lists ,Tuples & Dictionary

3: Conditional Statements & Loops

if…statement

if…else statement

elif…statement

The while…Loop

The for….Loop

4: Control Statements

continue statement

break statement

pass statement

5: Functions

Define function

Calling a function

Function arguments

Built-in functions

6: Modules & Packages

Modules

How to import a module…?

Packages

How to create packages

7: Classes & Objects

Introduction about classes & objects

Creating a class & object

Inheritance

Methods Overriding

Data hiding

8: Files & Exception Handling

Writing data to a file

Reading data from a file

Read and Write data from csv file

try…except

try…except…else

finally

os module

Module 2:Introduction to Machine learning(ML)

What is Machine learing?

Overview about sci-kit learn and tensorflow

Types of ML

Some complementing fields of ML

ML algorithms

Machine learning examples

Module 3:NumPy Arrays

Creating multidimensional array

NumPy-Data types

Array attributes

Indexing and Slicing

Creating array views and copies

Manipulating array shapes

I/O with NumPy

Module 4:Working with Pandas

Installing pandas

Pandas dataframes

Pandas Series

Data aggregation with Pandas DataFrames

Concatenating and appending DataFrames

Joining DataFrames

Handling missing data

Module 5: Python Regular Expressions

What are regular expressions?

The match Function

The search Function

Matching vs searching

Search and Replace

Extended Regular Expressions

Wildcard

Module 6:Python Oracle Database Access

Install the cx_Oracle and other Packages

Create Database Connection

CREATE, INSERT, READ, UPDATE and DELETE Operation

DML and DDL Oepration with Databases

Performing Transactions

Handling Database Errors

Disconnecting Database

Module 7:Web Scraping in Python

Module 8:Regression based learning

Simple regression

Multiple regression

Logistic regression

Predicting house prices with regression

Module 9:Clustering based learning

Defnition

Types of clustering

The k-means clustering algorithm

Module 5:Data mining

Introducing data mining

Decision Tree

Affiity Analysis

Clustering

Module 6:Classifiation – Sentiment Analysis

Module 7:Natural Language Processing

Install nltk

Tokenize words

Tokenizing sentences

Stop words with NLTK

Stemming words with NLTK

Speech tagging

Sentiment analysis with NLTK

Module 8:Making Sense of data through Visualization

Introducing matplotlib

Bar Charts

Line Charts

Scatter plots

Bubble charts

Module 9:Working with openCV

Setting up opencv

Loading and displaying images

Applying image filters

Tracking faces

Face recognition

Module 10:Performing predictions with Linear Regression

Simple linear regression

Multiple regression

Training and testing model

Projects:-

Note: Depends upon Trainers above projects may vary