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    What is Python?

    Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant indentation. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

    Why Python and not other Language?

    Easily readable language, Formatting is visually uncluttered, Better Memory Management

    python logo

    Pythons Core Design Philosophy:

    Prerequisite For Python Full Stack Developer

    Prerequisite For Python Full Stack Developer

    Objectives of Python Full Stack Developer Program

    The objective of this course in SDLC, is to help the participants to understand from basic to advance level in the application of Python. And we provide hands on real time knowledge on multiple top level Python advanced functionality in Full Stack development, with the help of 15+ years industry experienced trainers, we provide excellent course material, resume building and industry based live projects. This is a perfect package to build an amazing career in the IT industry.

    Why Python Full Stack Developer Training?

    Full stack developer is a term that has been on the rise increasingly. These web developers are always in demand. Python full stack developers are a subset of this full stack web developer. They are named so as they specialise working with this one language Python.

    python page background

    Highly Interactive with other platforms

    Used by top-notch companies

    Extensive support libraries

    Easy to learn

    python statistics

    Why SDLC ?

    Future & Key Benefits of Python Full Stack Development

    benefits for python full stack developer

    Full Stack Python Content

    Chapter 1: An Introduction to Python

    1.1 Python

    1.2 How to Install Python

    1.3 Difference between 2.x and 3.x 5

    1.4 Features of Python

    Chapter 2: Beginning Python Basics

    2.1. The print statement


    2.3. Python Data Structures & Data Types

    2.4. String Operations in Python

    2.5. Simple Input & Output

    2.6. Simple Output Formatting

    2.7. Format Function

    Chapter 3: Python Program Flow

    3.1. Indentation

    3.2. The If statement and its’ related statement

    3.3. An example with if and its related statement

    3.4. The while loop

    3.5. The for loop

    3.6. The range statement

    3.7. Break & Continue

    3.8. Assert

    3.9. Examples for looping

    Chapter 4: Functions & Modules

    4.1. Create your own functions

    4.2. Functions Parameters

    4.3. Variable Arguments

    4.4. Scope of a Function

    4.5. Function Documentation/ Docstrings

    4.6. Lambda Functions & map

    4.7. An Exercise with functions

    4.8. Create a Module

    4.9. Standard Modules

    4.10. args and **kwargs

    Chapter 5: Exceptions Handling

    5.1. Errors 10

    5.2. Exception handling with try

    5.3. Handling Multiple Exceptions

    5.4. Writing your own Exceptions

    Chapter 6: File Handling

    6.1. File Handling Modes

    6.2. Reading Files

    6.3. Writing & Appending to Files

    6.4. Handling File Exceptions

    6.5. The with statement

    6.6. Tell and Seek Functions

    Chapter 7: OOPS Concept

    7.1. Class and Object

    7.2. Creating Classes

    7.3. Instance Methods

    7.4. Inheritance

    7.5. Polymorphism

    7.6. Exception Classes & Custom Exceptions

    7.7 Types of Inheritance

    7.8 Encapsulation and Data Abstraction


    Chapter 8: Regular Expressions

    8.1 Simple Character Matches

    8.2 Special Characters

    8.3 Character Classes

    8.4 Quantifiers

    8.5 The Dot Character

    8.6 Greedy Matches

    8.7 Grouping

    8.8 Matching at Beginning or End

    8.9 Match Objects

    8.10 Substituting

    8.11 Splitting a String

    8.12 Compiling Regular Expressions

    Chapter 9: Advance Topics

    9.1 List Comprehensions

    9.2 Nested List Comprehensions

    9.3 Dictionary Comprehensions

    9.4 Functions

    9.5 Default Parameters

    9.6 Variable Arguments

    9.7 Specialized Sorts

    9.8 Iterators

    9.9 Generators

    Chapter 10: Miscellaneous Topics

    10.1 OS

    10.2 CSV

    10.3 Requests

    10.4 Shallow copy and Deep copy

    10.5 JSON

    10.6 Enumerator, zip and range

    10.7 operator overloading

    10.8 .Executing Modules as Scripts.

    Chapter 11: Date and Time Module

    Chapter 12. Lambda, Map and Filter Functions

    Chapter 13. Debugging

    Chapter 14. Interview Questions

    Chapter 14.Mock interview

    Chapter 15. Mock written test

    Chapter 16. Interview programs

    • Introduction

      • Introduction to DBMS
      • Introduction to Data Management
      • File system concepts
      • Database Models
      • Hierarchal Model
      • Network Model
      • Relational Model


    • Introduction to Oracle
      • History of Oracle
      • Oracle products and features
      • Oracle versions and releases
      • About SQL & SQL*PLUS
      • SQL*NET


    • Introduction to SQL
      • Sub-language Commands
      • Data Retrieval Language (DRL)
      • Data Definition Language (DDL)
      • Data Manipulation Language (DML)
      • Transaction Control Language (TCL)
      • Database security and Privileges


    • Working with DRL
      • Select statement
      • Distinct Selection
      • ‘Where’ Clause
      • ‘Order by’ Clauses


    • Working with DDL and DML
      • DDL commands
      • Database objects and object types
      • Working with Tables
      • Data Types
      • Table partitions Create, Alter,
      • Add, Modify, Drop Insert, Update,
      • Delete
      • Commit, Rollback


    • Oracle Built-in Functions
      • Arithmetic Functions Character
      • Functions Date Functions Queries
      • with Single Row Functions
      • Misc Functions
      • Working with Regular Expressions


    • Constraints
      • Data Integrity Concepts
      • Different types of Constraints
      • Table Level and Column Level
      • Composite Constraints
      • Check Constraint and
      • Restrictions Referential Integrity


    • Sub Queries
      • Normal Sub Queries.
      • Co-related Sub Queries.
      • Updating Using Co-Related.
      • Deleting Co-Related
      • Using Exists and Not Exists
      • Inline Queries
      • Miscellaneous Sub-queries.


    • Joins
      • Joining Tables
      • Simple Joins
      • Outer Joins
      • Self Joins
      • Joins with Inline Queries
      • ANSI Join Syntax


    • Select Statement with Misc Options
      • Set Operators
      • Pseudo Columns


    • Views, Synonyms & Indexes
      • Simple & Complex Views
      • Modifying the View Query
      • Synonyms
      • Indexes
      • Sequences



    • Introduction to PL/SQL
      • Role of PL/SQL in Applications
      • Programming Fundamentals
      • Restriction of SQL in PL/SQL
      • PL/SQL Engine
      • Working with PL/SQL
      • Blocks Conditional Structure


    • Anonymous Blocks
      • Declare
      • Begin
      • End
      • Conditional Structure
      • Loop Structure


    • Exception Handling
      • System Defined Exceptions
      • User Defined Exceptions
      • Defining Own Error Messages
      • Program Exception Int


    • Cursors
      • Introduction to
      • Types of Cursors
      • For Cursor
      • Cursor Attributes
      • Working with Implicit
      • Cursors
      • Dynamic Cursors


    • Stored Subprograms
      • Introduction of Stored
      • Subprograms Working with
      • Stored Procedures Overloading of
      • Subprogram Limitations of
      • Functions Understanding of
      • Packages Type Spec & Body
      • Package Instantiation
      • Calling Packages &
      • Procedures & Functions


    • Database Triggers
      • Introduction to Triggers
      • Statement Level
      • Triggers Row Level
      • Triggers When Clause
      • Co-relations Trigger
      • Predicates
      • Instead of Triggers Compound
      • Triggers Mutation of Triggers


    • Advanced Features
      • Autonomous Transactions
      • Dynamic SQL

    Class 1:

    • Course overview
    • Introduction
      1. About Django
      2. Django components
      3. Django design principles
      4. Prerequisites
      5. The demo project
      6. Course overview


    Class 2:

    • Installing Django
      1. Before you start: versions, virtualenvs and terminals
      2. Creating and activating a virtual environment
      3. Review virtual environments
      4. Installing django


    Class 3:

    • Starting a Django project
      1. Starting and running project
      2. Opening project in pycharm
      3. Adding a simple page
      4. Startprojects, views and url mappings


    Class 4:

    • Apps, Models and migrations
      1. Concepts of apps , models and migration
      2. Running initial migrations
      3. Creating an app
      4. Adding your own models
      5. Creating and running migrations
      6. Another migration


    Class 5:

    • url configuration
      1. about urlconf’s
      2. regular expression
      3. Expression example
      4. Passing url arguments


    Class 6 and 7:

    • Database Models
      1. About database models
      2. Understanding Django apps
      3. Configuring Django for database access
      4. Defining Django models
      5. Understanding model fields and options
    • Admin site and model apis
      1. Django admin sites
      2. Django models and __str__()
      3. Customizing the models with a ModelAdmin class
      4. The admin interface
      5. The model API


    Class 8 and 9:

    • Templates
      1. Django templates
      2. Loading template files
      3. Template tags
      4. Passing data from view to templates
      5. Displaying data in a template
      6. More on for loops
      7. A custom model manager and Q()
      8. Templates and data examples


    Class 10:

    • Forms and authentication
      1. Form classes
      2. Login and logout views
      3. Authentication
      4. Showing and invitation form
      5. General flow
      6. Handling form submit
      7. Tuning and styling form


    Class 11 and 12:

    • Working with APIs
      1. Connecting to github api
      2. Reading a file from repo
      3. Modifying content of file in specific branch
      4. Using other APIs
    • Django rest framework
      1. Creating your won REST apis

    Python Topic
    conditional statement
    module and Package
    OOPS concept
    Regular expression
    what is Selenium?
    download pycharm ,eclipse and sublime
    how to set up in machine.
    what is element?
    Finding Elements
    Using developer tools to find locators
    Inspecting pages and elements with Firefox using the Firebug add-in
    Inspecting pages and elements with Google Chrome
    Inspecting pages and elements with Internet Explorer
    Finding elements with Selenium WebDriver
    Using the find methods
    Finding elements using the ID attribute
    Finding elements using the name attribute
    Finding elements using the class name
    Finding elements using the tag name
    Finding elements using XPath
    Finding elements using CSS selectors
    Finding links
    Finding links with partial text
    Elements of HTML forms
    Understanding the WebDriver class
    Properties of the WebDriver class
    Methods of the WebDriver class
    Understanding the WebElement class
    Properties of the WebElement class
    Methods of the WebElement class
    Working with forms, textboxes, checkboxes, and radio buttons
    Checking whether the element is displayed and enabled
    Finding the element attribute value
    Using the is_selected() method
    Using the clear() and send_keys() methods
    Working with dropdowns and lists
    Understanding the Select class
    Properties of the Select class
    Methods of the Select class
    Working with alerts and pop-up windows
    Understanding the Alert class
    Properties of the Alert class
    Methods of the Alert class
    Automating browser navigation
    Using implicit wait
    Using explicit wait
    The expected condition class
    Waiting for an element to be enabled
    Waiting for alerts
    Implementing custom
    Methods for performing keyboard and mouse actions
    Keyboard actions
    The mouse movement
    The double_click method
    The drag_and_drop method
    Executing JavaScript
    Capturing screenshots of failures
    Recording a video of the test run
    Handling pop-up windows
    Managing cookies
    Behavior-Driven Development
    Installing Behave
    Writing the first feature in Behave
    Implementing a step definition file for the feature
    Creating environment configurations
    Running features
    Using a scenario outline
    CI with Jenkins
    Preparing for Jenkins
    Setting up Jenkins

    Goal – Get an overview of the python which is required to work on data science

    Objectives – At the end of this Module, you should be able understand the following topics

    • Lists
    • Tuples
    • Dictionaries
    • Sets
    • Importing packages
    • If else
    • Loops
    • Comprehensions
    • Functions
    • Map
    • Filter
    • Reduce
    • Numpy

    Data Preprocessing

    • Welcome to Part  – Data Preprocessing
    • Pandas Libraries
    • Get the dataset
    • Importing the Libraries
    • Importing the Dataset
    • For Python learners, summary of Object-oriented programming classes & objects
    • Missing Data
    • Categorical Data
    • Splitting the Dataset into the Training set and Test set
    • Feature Scaling
    • And here is our Data Preprocessing Template!
    • Quiz  Data Preprocessing

    —————————— Part  Regression ——————————

    • Welcome to Part  – Regression

     Simple Linear Regression

    • How to get the dataset
    • Dataset + Business Problem Description
    • Simple Linear Regression Intuition –   
    • Simple Linear Regression in Python –  
    • Quiz  Simple Linear Regression

    Support Vector Regression (SVR)

    • How to get the dataset
    • SVR in Python


    Decision Tree Regression

    • Decision Tree Regression Intuition
    • How to get the dataset
    • Decision Tree Regression in Python


     Random Forest Regression

    • Random Forest Regression Intuition
    • How to get the dataset
    • Random Forest Regression in Python
    • Random Forest Regression in R


     Evaluating Regression Models Performance

    • R-Squared Intuition
    • Adjusted R-Squared Intuition
    • Evaluating Regression Models Performance – ‘s Final Part
    • Interpreting Linear Regression Coefficients
    • Conclusion of Part  – Regression

     —————————- Part  Classification —————————-

    • Welcome to Part  – Classification

     Logistic Regression

    • Logistic Regression Intuition
    • How to get the dataset
    • Logistic Regression in Python –  
    • Python Classification Template
    • Logistic Regression in R –   
    • R Classification Template
    • Quiz  Logistic Regression


     K-Nearest Neighbors (K-NN)

    • K-Nearest Neighbor Intuition
    • How to get the dataset
    • K-NN in Python
    • K-NN in R
    • Quiz  K-Nearest Neighbor


    Support Vector Machine (SVM)

    • SVM Intuition
    • How to get the dataset
    • SVM in Python
    • SVM in R
      • SVMzip


    Kernel SVM

    • Kernel SVM Intuition
    • Mapping to a higher dimension
    • The Kernel Trick
    • Types of Kernel Functions
    • How to get the dataset
    • Kernel SVM in Python
    • Kernel SVM in R


    Naive Bayes

    • Bayes Theorem
    • Naive Bayes Intuition
    • Naive Bayes Intuition (Challenge Reveal)
    • Naive Bayes Intuition (Extras)
    • How to get the dataset
    • Naive Bayes in Python
    • Naive Bayes in R


     Evaluating Classification Models Performance

    • False Positives & False Negatives
    • Confusion Matrix
    • Accuracy Paradox
    • CAP Curve
    • CAP Curve Analysis
    • Conclusion of Part  – Classification

     —————————- Part  Clustering —————————-

    • Welcome to Part  – Clustering

     K-Means Clustering

    • K-Means Clustering Intuition
    • K-Means Random Initialization Trap
    • K-Means Selecting The Number Of Clusters
    • How to get the dataset
    • K-Means Clustering in Python
    • K-Means Clustering in R
    • Quiz  K-Means Clustering


    Hierarchical Clustering

    • Hierarchical Clustering Intuition
    • Hierarchical Clustering How Dendrograms Work
    • Hierarchical Clustering Using Dendrograms
    • How to get the dataset
    • HC in Python –   
    • HC in R –   
    • Quiz  Hierarchical Clustering
    • Conclusion of Part  – Clustering

     ———————— Part  Reinforcement Learning ————————

    • Welcome to Part  – Reinforcement Learning

    Upper Confidence Bound (UCB)

    • The Multi-Armed Bandit Problem
    • Upper Confidence Bound (UCB) Intuition
    • How to get the dataset
    • Upper Confidence Bound in Python –    


     ——————— Part  Natural Language Processing ———————

    • Welcome to Part  – Natural Language Processing
    • How to get the dataset
    • Natural Language Processing in Python –   
    • Challenge  
    • Challenge

     —————————- Part  Deep Learning —————————-

    • Welcome to Part  – Deep Learning
    • What is Deep Learning?


     Artificial Neural Networks

    • Plan of attack
    • The Neuron
    • The Activation Function
    • How do Neural Networks work?
    • How do Neural Networks learn?
    • Gradient Descent
    • Stochastic Gradient Descent
    • Backpropagation
    • How to get the dataset
    • Business Problem Description
    • ANN in Python –   – Installing Theano, Tensorflow and Keras
    • ANN in R –   
    • ANN in R –   (Last )

     Convolutional Neural Networks

    • Plan of attack
    • What are convolutional neural networks?
    • – Convolution Operation
    • (b) – ReLU Layer
    • – Pooling
    • – Flattening
    • – Full Connection
    • Summary
    • Softmax & Cross-Entropy
    • How to get the dataset
    • CNN in Python –  


    Recurrent Neural Network

    • Understanding a Recurrent Neuron in Detail
    • What are Recurrent Neural Networks (RNNs)?
    • Implementation of RNN in Keras
    • Vanishing and Exploding Gradient Problem
    • Long short-term memory


    Architecture of LSTM

    • Forget Gate
    • Input Gate
    • Output Gate
    • Improvement over RNN: LSTM (Long Short-Term Memory) Networks


     What is Generative Adversary Networks


     ———————– Part  Dimensionality Reduction ———————–

    • Welcome to Part  – Dimensionality Reduction

     Principal Component Analysis (PCA)

    • How to get the dataset
    • PCA in Python –   
    • PCA in R –  

    Linear Discriminant Analysis (LDA)

    • How to get the dataset
    • LDA in Python
    • LDA in R

     Kernel PCA

    • How to get the dataset
    • Kernel PCA in Python
    • Kernel PCA in R

     ——————— Part  Model Selection & Boosting ———————

    • Welcome to Part  – Model Selection & Boosting

    Model Selection

    • How to get the dataset
    • k-Fold Cross Validation in Python
    • Grid Search in Python –   



    Hit The Ground Running
    Welcome to the R Programming Course!
    Installing R and R Studio (MAC & Windows)
    Exercise – Get Excited!

    Core Programming Principles
    Welcome to this section. This is what you will learn!
    Types of variables
    Using Variables
    Logical Variables and Operators
    The “While” Loop
    Using the console
    The “For” Loop
    The “If” statement
    Section Recap
    HOMEWORK: Law of Large Numbers
    Core Programming Principles

    Fundamentals Of R
    Welcome to this section. This is what you will learn!
    What is a Vector?
    Let’s create some vectors
    Using the [] brackets
    Vectorized operations
    The power of vectorized operations
    Functions in R
    Packages in R
    Section Recap
    HOMEWORK: Financial Statement Analysis
    Fundamentals of R

    Welcome to this section. This is what you will learn!
    Project Brief: Basketball Trends
    Building Your First Matrix
    Naming Dimensions
    Colnames() and Rownames()
    Matrix Operations
    Visualizing With Matplot()
    Visualizing Subsets
    Creating Your First Function
    Basketball Insights
    Section Recap
    HOMEWORK: Basketball Free Throws

    Data Frames
    Welcome to this section. This is what you will learn!
    Project Brief: Demographic Analysis
    Importing data into R
    Exploring your dataset
    Using the $ sign
    Basic operations with a Data Frame
    Filtering a Data Frame
    Introduction to qplot
    Visualizing With Qplot: Part I
    Building Dataframes
    Merging Data Frames
    Visualizing With Qplot: Part II
    Section Recap
    HOMEWORK: World Trends
    Data Frames

    Advanced Visualization With GGPlot2
    Welcome to this section. This is what you will learn!
    Project Brief: Movie Ratings
    Grammar Of Graphics – GGPlot2
    What is a Factor?
    Plotting With Layers
    Overriding Aesthetics
    Mapping vs Setting
    Histograms and Density Charts
    Starting Layer Tips
    Statistical Transformations
    Using Facets
    Perfecting By Adding Themes
    Section Recap
    HOMEWORK: Movie Domestic % Gross
    Advanced Visualization With GGPlot2

    Will be Updated Soon

    Python Fundamentals and Programming

    • Introduction to Python Strings,
    • Lists and Tuples
    • Dictionaries
    • Sets Conditional Execution & Loops Comprehensions
    • Functions
    • Modules
    • Namespaces

     Intermediate Python

    • File Handling
    • Object Oriented Programming
    • Iterator,
    • Generator,
    • Decorators
    • Lambda Expressions
    • Writing Library Building
    • Closure
    • Function Factory
    • Method Chaining Exception Handling,
    • Context Manager
    • Multithreading
    • Multiprocessing
    • Regular Expression
    • OOPS Concept

    Advanced Python:

    • Scipy
    • Pandas
    • Scikit-learn
    • Matlotlib
    • Numpy
    • Python integration with Hadoop and spark
    • Web scrapping and BeautifulSoup



    • Machine Learning
    • Data Preprocessing
    • Supervised Learning
    • Feature Engineering

    Supervised Learning-Classification

    • Unsupervised learning
    • Time Series Modelling
    • Ensemble Learning
    • Recommender Systems
    • Text Mining
    • Machine Learning with Tensorflow for Beginners
    • Machine Learning Project 1
    • Machine Learning Project 2
    • Natural Language Processing (NLP)
    • Bayesian Machine Learning AB Testing
    • Business Intelligence Publisher using Siebel
    • BI – Business Intelligence


    Artificial Intelligence


    • Decoding Artificial Intelligence
    • Fundamentals of Machine Learning
    • Deep Learning Lesson
    • Machine Learning Workflow
    • Performance Metrics
    • Neural Network
    • Computer Vision
    • NLP
    • Sequential Models and NLP
    • Advanced Computer Vision
    • Introduction to GAN

    Introduction of HTML, CSS
    Welcome & What We’re Learning
    What is HTML & CSS?
    HTML Tags, Attributes & Elements
    File & Folder Naming Conventions
    Typical Website File & Folder Structure
    Managing Your Production Files
    Tools of the Trade

    HTML Foundations: Part I
    Starting Your First Web Page
    The Doctype
    The Basic Structure of an HTML Document
    Page Title
    Emphasis & Strong Emphasis
    HTML Parent/Child Structure

    HTML Foundations: Part II

    HTML Foundations: Part III
    HTML Special Characters

    HTML Foundations: Part IV
    IDs & Classes
    Span & Div
    Header & Footer
    Nav, Section & Article
    Abbreviations & Quotes

    CSS Foundations: Part I
    The Style Rule
    Inline styles
    Internal styles
    External styles
    CSS Selectors, Properties & Values
    Inheritance of Styles
    Pixels, Percentages, Points & Ems!

    CSS Foundations: Part II
    ID Selectors
    Class Selectors
    Descendant Selectors
    Grouping Selectors
    Get Your Hands Dirty!

    CSS Foundations: Part III
    The Box Model
    Colours (or Colors)
    Text Styling & Formatting
    Sexy Typography
    Background Images
    Styling Forms

    CSS Foundations: Part IV
    Styling Links
    Block & Inline Elements
    Float & Clear
    CSS Positioning
    CSS Specificity

    Putting It All Together
    Final Website Walk Through
    About the Course Files
    HTML: Coding the Header & Hero
    HTML: Coding the General Content
    HTML: Coding the News & Events
    HTML: Coding the Footer
    CSS: Adding Normalize.css
    CSS: General Styles & Typography
    CSS: Styling the Header
    CSS: Styling the Hero
    CSS: Styling the General Content
    CSS: Styling the News & Events
    CSS: Styling the Footer
    The Finished Product & Conclusion

    Introduction to Javascript
    “Hello World” with Javascript
    Little History
    Using ‘REPL’ style console
    Data Types
    Flavor of Javascript
    The String data type
    The Number data type
    The Boolean data type
    ‘if-else’ statement
    ‘switch’ statement
    The ‘while’ loop
    The ‘for’ loop
    ‘break’ and ‘continue’
    Variable Scope
    Type Conversion
    Javascript in a web page
    Using external Javascript files

    Objects & Arrays
    What are objects?
    Object properties
    JSON- Javascript Object Notation
    The ‘global’ object
    Arrays in Javascript
    Sparse Arrays
    Arrays as objects
    Length of an array
    Iterating over an array
    Deleting elements of an array
    Methods in an array
    Sorting arrays

    Functional Programming with Javascript
    Functional paradigm
    Higher Order functions
    Anonymous functions
    Nested functions
    List Comprehension – filter function
    List Comprehension – map function
    List Comprehension – reduce function
    Accessing variable number of arguments

    Object Oriented Programming with Javascript
    Functions as Objects
    The ‘this’ keyword
    Constructor functions
    Using the ‘prototype’ property
    Inheritance using ‘prototype’

    Error handling
    DOM: Document Object Model
    A simple slideshow
    ‘setTimeout’ function
    Browser events
    Event bubbling and propagation
    The event object
    AJAX overview
    Using XMLHttpRequest object
    Using var keyword for local variables
    How browsers work


    Course Introduction
    What is Angular
    Architecture of Angular Apps
    Setting Up the Development Environment
    Your First Angular App
    Structure of Angular Projects
    Angular Version History
    How to Take This Course


    What is TypeScript
    Your First TypeScript Program
    Declaring Variables
    Type Assertions
    Arrow Functions
    Access Modifiers
    Access Modifiers in Constructor Parameters


    Building Blocks of Angular Apps
    Generating Components Using Angular CLI
    Dependency Injection
    Generating Services Using Angular CLI
    Authors Page


    Property Binding
    Attribute Binding
    Adding Bootstrap
    Class Binding
    Style Binding
    Event Binding
    Event Filtering
    Template Variables
    Two-way Binding
    Custom Pipes
    Favorite Component
    Title Casing


    Component API
    Input Properties
    Aliasing Input Properties
    Output Properties
    Passing Event Data
    Aliasing Output Properties
    View Encapsulation
    Like Component


    Hidden Property
    ngFor and Change Detection
    ngFor and trackBy
    The Leading Asterisk
    Safe Traversal Operator
    Creating Custom Directives


    Building a Bootstrap Form
    Types of Forms
    Adding Validation
    Specific Validation Errors
    Styling Invalid Input Fields
    Cleaner Templates
    Control Classes and Directives
    Disabling the Submit Button
    Working with Check Boxes
    Working with Drop-down Lists
    Working with Radio Buttons
    Course Form


    Building a Bootstrap Form
    Creating Controls Programmatically
    Adding Validation
    Specific Validation Errors
    Implementing Custom Validation
    Asynchronous Operations
    Asynchronous Validation
    Showing a Loader Image
    Validating the Form Upon Submit
    Nested FormGroups
    Quick Recap
    Change Password Form


    Getting Data
    Creating Data
    Updating Data
    Deleting Data
    OnInit Interface
    Separation of Concerns
    Extracting a Service
    Handling Errors
    Handling Unexpected Errors
    Handling Expected Errors
    Throwing Application-specific Errors
    Handling Bad Request Errors
    Importing Observable Operators and Factory Methods
    Global Error Handling
    Extracting a Reusable Error Handling Method
    Extracting a Reusable Data Service
    The Map Operator
    Optimistic vs Pessimistic Updates
    Observables vs Promises
    GitHub Followers Page


    Routing in a Nutshell
    Configuring Routes
    Getting the Route Parameters
    Why Route Parameters Are Observable
    Routes with Multiple Parameters
    Query Parameters
    Subscribing to Multiple Observables
    SwitchMap Operator
    Programmatic Navigation
    Blog Archives




    The Java real project interfaces (APIs) are the foundation of the Java Platform,

    Standard Edition (Java SE). They are used in all classes of Java programming,

    from desktop applications to J2EE applications.

    After Completing this Module, You will be ready to:

    Appear in exams on java under any technical university of India.

    Develop Desktop applications,  Multi-threaded programs in java.

    Appear in SCJP exams

    Update yourself with Advance frameworks of java.

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