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

2.2. Comments

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

PL/SQL COURSE CONTENT

• 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
  • SQLCODE, SQLERRM

• 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
Introduction
Basic
Variables
Datatypes
Integer
String
List
Tuple
Dictionary
conditional statement
Loops
Functions
File
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

GAN

 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 –   

PROJECT

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

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

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?
Aesthetics
Plotting With Layers
Overriding Aesthetics
Mapping vs Setting
Histograms and Density Charts
Starting Layer Tips
Statistical Transformations
Using Facets
Coordinates
Perfecting By Adding Themes
Section Recap
HOMEWORK: Movie Domestic % Gross
Advanced Visualization With GGPlot2

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

• 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

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

TYPESCRIPT FUNDAMENTALS

Introduction
What is TypeScript
Your First TypeScript Program
Declaring Variables
Types
Type Assertions
Arrow Functions
Interfaces
Classes
Objects
Constructors
Access Modifiers
Access Modifiers in Constructor Parameters
Properties
Modules
Exercise
Solution

ANGULAR FUNDAMENTALS

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

DISPLAYING DATA AND HANDLING EVENTS

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

BUILDING REUSABLE COMPONENTS

Introduction
Component API
Input Properties
Aliasing Input Properties
Output Properties
Passing Event Data
Aliasing Output Properties
Templates
Styles
View Encapsulation
ngContent
ngContainer
Like Component

DIRECTIVES

Introduction
ngIf
Hidden Property
ngSwitchCase
ngFor
ngFor and Change Detection
ngFor and trackBy
The Leading Asterisk
ngClass
ngStyle
Safe Traversal Operator
Creating Custom Directives
ZippyComponent

TEMPLATE-DRIVEN FORMS

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

REACTIVE FORMS

Introduction
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
FormArray
FormBuilder
Quick Recap
Change Password Form

CONSUMING HTTP SERVICES

Introduction
JSONPlaceHolder
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 AND NAVIGATION

Introduction
Routing in a Nutshell
Configuring Routes
RouterOutlet
RouterLink
RouterLinkActive
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