Python Self Learning Videos Complete Course (Download)

Python Training Overview

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language.

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis

What are the Python Course Pre-requisites

There are no hard pre-requisites.

Basic understanding of Computer Programming terminologies is sufficient. Also, basic concepts related to Programming and Database is beneficial but not mandatory.

Objectives of the Course

To understand the concepts and constructs of Python to create own Python programs, know the machine learning algorithms in Python and work on a real-time project running on Python.

Who should do the course

Big Data Professionals

IT Developers

Those who are showing interest to build their career in Python

Python Training Course Duration

35 Days, Daily 1

Python Course Content

Core Python

Introduction to Languages

What is Language?

Types of languages

Introduction to Translators

Compiler

Interpreter

What is Scripting Language?

Types of Script

Programming Languages v/s Scripting Languages

Difference between Scripting and Programming languages

What is programming paradigm?

Procedural programming paradigm

Object Oriented Programming paradigm

Introduction to Python

What is Python?

WHY PYTHON?

History

Features – Dynamic, Interpreted, Object oriented, Embeddable, Extensible, Large standard libraries, Free and Open source

Why Python is General Language?

Limitations of Python

What is PSF?

Python implementations

Python applications

Python versions

PYTHON IN REALTIME INDUSTRY

Difference between Python 2.x and 3.x

Difference between Python 3.7 and 3.8

Software Development Architectures

Python Software’s

Python Distributions

Download &Python Installation Process in Windows, Unix, Linux and Mac

Online Python IDLE

Python Real-time IDEs like Spyder, Jupyter Note Book, PyCharm, Rodeo, Visual Studio Code, ATOM, PyDevetc

Python Language Fundamentals

Python Implementation Alternatives/Flavors

Keywords

Identifiers

Constants / Literals

Data types

Python VS JAVA

Python Syntax

Different Modes of Python

Interactive Mode

Scripting Mode

Programming Elements

Structure of Python program

First Python Application

Comments in Python

Python file extensions

Setting Path in Windows

Edit and Run python program without IDE

Edit and Run python program using IDEs

INSIDE PYTHON

Programmers View of Interpreter

Inside INTERPRETER

What is Byte Code in PYTHON?

Python Debugger

Python Variables

bytes Data Type

byte array

String Formatting in Python

Math, Random, Secrets Modules

Introduction

Initialization of variables

Local variables

Global variables

‘global’ keyword

Input and Output operations

Data conversion functions – int(), float(), complex(), str(), chr(), ord()

Operators

Arithmetic Operators

Comparison Operators

Python Assignment Operators

Logical Operators

Bitwise Operators

Shift operators

Membership Operators

Identity Operators

Ternary Operator

Operator precedence

Difference between “is” vs “==”

Input & Output Operators

Print

Input

Command-line arguments

Control Statements

Conditional control statements

If

If-else

If-elif-else

Nested-if

Loop control statements

for

while

Nested loops

Branching statements

Break

Continue

Pass

Return

Case studies

Data Structures or Collections

Introduction

Importance of Data structures

Applications of Data structures

Types of Collections

Sequence

Strings, List, Tuple, range

Non sequence

Set, Frozen set, Dictionary

Strings

What is string

Representation of Strings

Processing elements using indexing

Processing elements using Iterators

Manipulation of String using Indexing and Slicing

String operators

Methods of String object

String Formatting

String functions

String Immutability

Case studies

List Collection

What is List

Need of List collection

Different ways of creating List

List comprehension

List indices

Processing elements of List through Indexing and Slicing

List object methods

List is Mutable

Mutable and Immutable elements of List

Nested Lists

List_of_lists

Hardcopy, shallowCopy and DeepCopy

zip() in Python

How to unzip?

Python Arrays:

Case studies

Tuple Collection

What is tuple?

Different ways of creating Tuple

Method of Tuple object

Tuple is Immutable

Mutable and Immutable elements of Tuple

Process tuple through Indexing and Slicing

List v/s Tuple

Case studies

Set Collection

What is set?

Different ways of creating set

Difference between list and set

Iteration Over Sets

Accessing elements of set

Python Set Methods

Python Set Operations

Union of sets

functions and methods of set

Python Frozen set

Difference between set and frozenset ?

Case study

Dictionary Collection

What is dictionary?

Difference between list, set and dictionary

How to create a dictionary?

PYTHON HASHING?

Accessing values of dictionary

Python Dictionary Methods

Copying dictionary

Updating Dictionary

Reading keys from Dictionary

Reading values from Dictionary

Reading items from Dictionary

Delete Keys from the dictionary

Sorting the Dictionary

Python Dictionary Functions and methods

Dictionary comprehension

Functions

What is Function?

Advantages of functions

Syntax and Writing function

Calling or Invoking function

Classification of Functions

No arguments and No return values

With arguments and No return values

With arguments and With return values

No arguments and With return values

Recursion

Python argument type functions :

Default argument functions

Required(Positional) arguments function

Keyword arguments function

Variable arguments functions

‘pass’ keyword in functions

Lambda functions/Anonymous functions

map()

filter()

reduce()

Nested functions

Non local variables, global variables

Closures

Decorators

Generators

Iterators

Monkey patching

Advanced Python

Python Modules

Importance of modular programming

What is module

Types of Modules – Pre defined, User defined.

User defined modules creation

Functions based modules

Class based modules

Connecting modules

Import module

From … import

Module alias / Renaming module

Built In properties of module

Packages

Organizing python project into packages

Types of packages – pre defined, user defined.

Package v/s Folder

py file

Importing package

PIP

Introduction to PIP

Installing PIP

Installing Python packages

Un installing Python packages

OOPs

Procedural v/s Object oriented programming

Principles of OOP – Encapsulation , Abstraction (Data Hiding)

Classes and Objects

How to define class in python

Types of variables – instance variables, class variables.

Types of methods – instance methods, class method, static method

Object initialization

‘self’ reference variable

‘cls’ reference variable

Access modifiers – private(__) , protected(_), public

AT property class

Property() object

Creating object properties using setaltr, getaltr functions

Encapsulation(Data Binding)

What is polymorphism?

Overriding

i) Method overriding

ii) Constructor overriding

Overloading

i) Method Overloading

ii) Constructor Overloading

iii) Operator Overloading

Class re-usability

Composition

Aggregation

Inheritance – single , multi level, multiple, hierarchical and hybrid inheritance and Diamond inheritance

Constructors in inheritance

Object class

super()

Runtime polymorphism

Method overriding

Method resolution order(MRO)

Method overriding in Multiple inheritance and Hybrid Inheritance

Duck typing

Concrete Methods in Abstract Base Classes

Difference between Abstraction & Encapsulation

Inner classes

Introduction

Writing inner class

Accessing class level members of inner class

Accessing object level members of inner class

Local inner classes

Complex inner classes

Case studies

Exception Handling & Types of Errors

What is Exception?

Why exception handling?

Syntax error v/s Runtime error

Exception codes – AttributeError, ValueError, IndexError, TypeError…

Handling exception – try except block

Try with multi except

Handling multiple exceptions with single except block

Finally block

Try-except-finally

Try with finally

Case study of finally block

Raise keyword

Custom exceptions / User defined exceptions

Need to Custom exceptions

Case studies

Regular expressions

Understanding regular expressions

String v/s Regular expression string

“re” module functions

Match()

Search()

Split()

Findall()

Compile()

Sub()

Subn()

Expressions using operators and symbols

Simple character matches

Special characters

Character classes

Mobile number extraction

Mail extraction

Different Mail ID patterns

Data extraction

Password extraction

URL extraction

Vehicle number extraction

Case study

File &Directory handling

Introduction to files

Opening file

File modes

Reading data from file

Writing data into file

Appending data into file

Line count in File

CSV module

Creating CSV file

Reading from CSV file

Writing into CSV file

Object serialization – pickle module

XML parsing

JSON parsing

Python Logging

Logging Levels

implement Logging

Configure Log File in over writing Mode

Timestamp in the Log Messages

Python Program Exceptions to the Log File

Requirement of Our Own Customized Logger

Features of Customized Logger

Date & Time module

How to use Date & Date Time class

How to use Time Delta object

Formatting Date and Time

Calendar module

Text calendar

HTML calendar

OS module

Shell script commands

Various OS operations in Python

Python file system shell methods

Creating files and directories

Removing files and directories

Shutdown and Restart system

Renaming files and directories

Executing system commands

Multi-threading & Multi Processing

Introduction

Multi tasking v/s Multi threading

Threading module

Creating thread – inheriting Thread class , Using callable object

Life cycle of thread

Single threaded application

Multi threaded application

Can we call run() directly?

Need to start() method

Sleep()

Join()

Synchronization – Lock class – acquire(), release() functions

Case studies

Garbage collection

Introduction

Importance of Manual garbage collection

Self reference objects garbage collection

‘gc’ module

Collect() method

Threshold function

Case studies

Python Data Base Communications(PDBC)

Introduction to DBMS applications

File system v/s DBMS

Communicating with MySQL

Python – MySQL connector

connector module

connect() method

Oracle Database

Install cx_Oracle

Cursor Object methods

execute() method

executeMany() method

fetchone()

fetchmany()

fetchall()

Static queries v/s Dynamic queries

Transaction management

Case studies

Python – Network Programming

What is Sockets?

What is Socket Programming?

The socket Module

Server Socket Methods

Connecting to a server

A simple server-client program

Server

Client

Tkinter & Turtle

Introduction to GUI programming

Tkinter module

Tk class

Components / Widgets

Label , Entry , Button , Combo, Radio

Types of Layouts

Handling events

Widgets properties

Case studies

Data analytics modules

Numpy

Introduction

Scipy

Introduction

Arrays

Datatypes

Matrices

N dimension arrays

Indexing and Slicing

Pandas

Introduction

Data Frames

Merge , Join, Concat

MatPlotLib introduction

Drawing plots

Introduction to Machine learning

Types of Machine Learning?

Introduction to Data science

DJANGO

Introduction to PYTHON Django

What is Web framework?

Why Frameworks?

Define MVT Design Pattern

Difference between MVC and MVT

PANDAS

Pandas – Introduction

Pandas – Environment Setup

Pandas – Introduction to Data Structures

Dimension & Description

Series

DataFrame

Data Type of Columns

Panel

Pandas — Series

Series

Create an Empty Series

Create a Series f

rom ndarray

rom dict

rom Scalar

Accessing Data from Series with Position

Retrieve Data Using Label (Index)

Pandas – DataFrame

DataFrame

Create DataFrame

Create an Empty DataFrame

Create a DataFrame from Lists

Create a DataFrame from Dict of ndarrays / Lists

Create a DataFrame from List of Dicts

Create a DataFrame from Dict of Series

Column Selection

Column Addition

Column Deletion

Row Selection, Addition, and Deletion

Pandas – Panel

Panel()

Create Panel

Selecting the Data from Panel

Pandas – Basic Functionality

DataFrame Basic Functionality

Pandas – Descriptive Statistics

Functions & Description

Summarizing Data

Pandas – Function Application

Table-wise Function Application

Row or Column Wise Function Application

Element Wise Function Application

Pandas – Reindexing

Reindex to Align with Other Objects

Filling while ReIndexing

Limits on Filling while Reindexing

Renaming

Pandas – Iteration

Iterating a DataFrame

iteritems()

iterrows()

itertuples()

Pandas – Sorting

By Label

Sorting Algorithm

Pandas – Working with Text Data

Pandas – Options and Customization

get_option(param)

set_option(param,value)

reset_option(param)

describe_option(param)

option_context()

Pandas – Indexing and Selecting Data

.loc()

.iloc()

.ix()

Use of Notations

Pandas – Statistical Functions

Percent_change

Covariance

Correlation

Data Ranking

Pandas – Window Functions

.rolling() Function

.expanding() Function

.ewm() Function

Pandas – Aggregations

Applying Aggregations on DataFrame

Pandas – Missing Data

Cleaning / Filling Missing Data

Replace NaN with a Scalar Value

Fill NA Forward and Backward

Drop Missing Values

Replace Missing (or) Generic Values

Pandas – GroupBy

Split Data into Groups

View Groups

Iterating through Groups

Select a Group

Aggregations

Transformations

Filtration

Pandas – Merging/Joining

Merge Using ‘how’ Argument

Pandas – Concatenation

Concatenating Objects

Time Series

Pandas – Date Functionality

Pandas – Timedelta

Pandas – Categorical Data

Object Creation

Pandas – Visualization

Bar Plot

Histograms

Box Plots

Area Plot

Scatter Plot

Pie Chart

Pandas – IO Tools

csv

Pandas – Sparse Data

Pandas – Caveats & Gotchas

Pandas – Comparison with SQL

NUMPY

NUMPY − INTRODUCTION

NUMPY − ENVIRONMENT

NUMPY − NDARRAY OBJECT

NUMPY − DATA TYPES

Data Type Objects (dtype)

NUMPY − ARRAY ATTRIBUTES

shape

ndim

itemsize

flags

NUMPY − ARRAY CREATION ROUTINES

empty

zeros

ones

NUMPY − ARRAY FROM EXISTING DATA

asarray

frombuffer

fromiter

NUMPY − ARRAY FROM NUMERICAL RANGES

arange

linspace

logspace

NUMPY − INDEXING & SLICING

NUMPY − ADVANCED INDEXING

Integer Indexing

Boolean Array Indexing

NUMPY − BROADCASTING

NUMPY − ITERATING OVER ARRAY

Iteration

Order

Modifying Array Values

External Loop

Broadcasting Iteration

NUMPY – ARRAY MANIPULATION

reshape

ndarray.flat

ndarray.flatten

ravel

transpose

ndarray.T

swapaxes

rollaxis

broadcast

broadcast_to

expand_dims

squeeze

concatenate

stack

hstack and numpy.vstack

split

hsplit and numpy.vsplit

resize

append

insert

delete

unique

NUMPY – BINARY OPERATORS

bitwise_and

bitwise_or

invert()

left_shift

right_shift

NUMPY − STRING FUNCTIONS

NUMPY − MATHEMATICAL FUNCTIONS

Trigonometric Functions

Functions for Rounding

NUMPY − ARITHMETIC OPERATIONS

reciprocal()

power()

mod()

NUMPY − STATISTICAL FUNCTIONS

amin() and numpy.amax()

ptp()

percentile()

median()

mean()

average()

Standard Deviation

Variance

NUMPY − SORT, SEARCH & COUNTING FUNCTIONS

sort()

argsort()

lexsort()

argmax() and numpy.argmin()

nonzero()

where()

extract()

NUMPY − BYTE SWAPPING

ndarray.byteswap()

NUMPY − COPIES & VIEWS

No Copy

View or Shallow Copy

Deep Copy

NUMPY − MATRIX LIBRARY

empty()

matlib.zeros()

matlib.ones()

matlib.eye()

matlib.identity()

matlib.rand()

NUMPY − LINEAR ALGEBRA

dot()

vdot()

inner()

matmul()

Determinant

linalg.solve()

NUMPY − MATPLOTLIB

Sine Wave Plot

subplot()

bar()

NUMPY – HISTOGRAM USING MATPLOTLIB

histogram()

plt()

NUMPY − I/O WITH NUMPY

save()

savetxt()