Python Course Content

Python Training in Kanpur

Core Python
Introduction to Python Fundamentals

Variable

Data type

Collection data types{list,tuple,set,frozenset, range,bytearray,bytes,dictionary}

String handling

Control statement(conditional , looping)

Function

• Types of arguments
• Positional,keyword,default,key-value pair,arbitrary,
• Decorator,generator

Command Line Argument

Exception handling

Modules

Packages

File IO

Object Oriented Programming introduction

Class and object

Inheritance

Polymorphism

Abstraction (abstract method and abstract class)

Encapsulation

Multithreading

Logging

Python database connectivity (SQLite, Sqlserver, Oracle, and Mysql)

Django Course Content

Introduction to WEB

What is Web?

Web Features?

W3C and W3C Members

Introduction to What WG

Core HTML

Introduction

Parts in HTML Document

Version Information

Head Section

Meta Information

Favicons

Body Section

HTML FORMS

Anchors, Images

Advance HTML5

Introduction

HTML5 HISTORY

Why HTML5?

New Features and Groups

Structure of HTML5 Document

Power of HTML5 and Features

Semantics and Block Level Elements

HTML5 Forms

HTML5 Multimedia

HTML5 Graphics

Core CSS

Introduction

CSS Basics

CSS Introduction

CSS Syntax

CSS Versions

CSS Id & Class

CSS Styling

Styling Backgrounds

Styling Text

Styling Fonts

CSS Borders

Advance CSS

Introduction

CSS3 Modules

Selectors

Box Model

Backgrounds and Borders

Text Effects

2D/3D Transformations

Core &Adanced Animations

Multiple Column Layout

User Interface

Core JavaScript

What is Script? Types of Scripts?

Introduction to JavaScript

Comments and Types of Comments

Popup Boxes

Variables & Operators

JavaScript Functions and Events

Conditional Statements

Looping Control Statement

Advance JavaScript

Types of Errors

Exception Handling

Java Script Objects

Browser Objects

Validations in JS

Django Web Framework

What is a Framework

Introduction to Django

Django – Design Philosophies

History of Django

Why django and Features

Environment setup

Web Server

MVC Pattern

MVC Architecture vs MVT Architecture

Django MVC – MVT Pattern

Getting Started with Django

Creating the first Project

Integrating the Project to sublime text

The Project Structure

Running the server

Solving the issues and Migrations

Database Setup

Setting Up Your Project

Create an Application

What Django Follows

Structure of django framework

Model Layer

What are models

Model fields

Querysets

Django – Admin Interface

Starting the Admin Interface

Migrations

Views Layer

Simple View

Basic view(displaying hello world)

Functional views, class based views

Django – URL Mapping

Organizing Your URLs

Role of urls in djnago

Working urls

Forms

Sending Parameters to Views

Templates layer

The Render Function

Django Template Language (DTL)

Role of template layer in django

Filters,Tags, Tag if, Tag for, Block and Extend Tags

Comment Tag, Usage of templates

Extending base template

Django – Models

Creating a Model

Manipulating Data (CRUD)

Linking Models

Django – Page Redirection

Django – Sending E-mails

Sending a Simple E-mail

Sending Multiple Mails with send_mass_mail

Sending HTML E-mail

Sending HTML E-mail with Attachments

Django – Form Processing

Using Form in a View

Usage of forms

Crud operations using forms

Crispy forms in django

Django – File Uploading

Uploading an Image

Django – Apache Setup

Django – Cookies Handling

Django – Sessions

Django – Comments

Django Admin

Creating Super User

Using admin in Django

Adding models to admin

Adding model objects using admin

Displaying in cmd using querysets

Static files

Loading css files into templates

Loading js files into templates

Uploading image using models

User authentication

Descriptive Statistics

Introduction to the course

Descriptive Statistics

Probability Distributions

Inferential Statistics

Inferential Statistics through hypothesis tests

Regression & ANOVA

Regression

ANOVA(Analysis of Variance)

Machine Learning: Introduction and Concepts

Differentiating algorithmic and model based frameworks

Regression : Ordinary Least Squares, Ridge Regression, Lasso Regression,

K Nearest Neighbours Regression & Classification

Supervised Learning with Regression and Classification techniques -1

Bias-Variance Dichotomy

Model Validation Approaches

Logistic Regression

Linear Discriminant Analysis

Quadratic Discriminant Analysis

Regression and Classification Trees

Support Vector Machines

Supervised Learning with Regression and Classification techniques -2

Ensemble Methods: Random Forest

Neural Networks

Deep learning

Unsupervised Learning and Challenges for Big Data Analytics

Clustering

Associative Rule Mining

Challenges for big data anlalytics

Prescriptive analytics

Creating data for analytics through designed experiments

Creating data for analytics through Active learning

Creating data for analytics through Reinforcement learning

-------------------- Part 1: Data Preprocessing --------------------

-------------------- Part 2: Regression --------------------

Simple Linear Regression

Multiple Linear Regression

Polynomial Regression

Support Vector Regression (SVR)

Decision Tree Regression

Random Forest Regression

Evaluating Regression Models Performance

-------------------- Part 3: Classification --------------------

Logistic Regression

K-Nearest Neighbors (K-NN)

Support Vector Machine (SVM)

Kernel SVM

Naive Bayes

Decision Tree Classification

Random Forest Classification

Evaluating Classification Models Performance

-------------------- Part 4: Clustering --------------------

K-Means Clustering

Hierarchical Clustering

-------------------- Part 5: Association Rule Learning --------------------

Apriori

Eclat

-------------------- Part 6: Reinforcement Learning --------------------

Upper Confidence Bound (UCB)

Thompson Sampling

-------------------- Part 7: Natural Language Processing --------------------

-------------------- Part 8: Deep Learning --------------------

Artificial Neural Networks

Convolutional Neural Networks

-------------------- Part 9: Dimensionality Reduction --------------------

Principal Component Analysis (PCA)

Linear Discriminant Analysis (LDA)

Kernel PCA