Python Training in Bangalore

/Python Training in Bangalore
Python Training in Bangalore 2017-09-30T14:48:42+00:00
Django Training        |        Data Science with Python       |       Machine Learning with Python

python-training-in-bangalore

Python Training in Bangalore

Python is fastest growing higher level programming / scripting language. Supports OOPs and classical way of implementation. In addition, the philosophy of Python syntax focuses on general programming audience. This makes programming easy readable and even understandable. The US school system even adopted Python as beginner programming language at school level. The wide application makes it to dominate the market when compared to other languages. The open source interpreters like jPython, ironPython helps run Python code in JAVA and .net environment with minute changes.The present trading Python fields where Web development, Data processing (Machine Learning, Business Intelligence, audio/image/video processing) Automation and Testing, IOT (Rasperri Pi), Networking and OS programming.

Python is easy to learn and allows us to solve any problems in less number of lines of code compared to other languages.Every release of Python adds new and advanced concepts like Meta programming, Template, Descriptor, Decorators, Generators and Iterators. More than in-built, wide availability of stable 3rd party packages more focused towards implementation in very simple way. The hackable nature of the language helps us to customize on our own for our own application. The implementation of Python over other JAVA, dotnet environments with jPython and ironPython adds more advantage.

Python Training

  • Why do we need Python?
  • Program structure
  • Interactive Shell
  • Executable or script files.
  • User Interface or IDE
  • Object creation and deletion
  • Object properties
  • Numbers
  • Strings
  • List
  • Tuple
  • Dictionary
  • Other Core Types
  • Assignments, Expressions and prints
  • If tests and Syntax Rules
  • While and For Loops
  • Iterations and Comprehensions
  • Opening a file
  • Using Files
  • Other File tools
  • Function definition and call
  • Function Scope
  • Arguments
  • Function Objects
  • Anonymous Functions
  • Module Creations and Usage
  • Module Search Path
  • Module Vs. Script
  • Package Creation and Importing
  • Classes and instances
  • Classes method calls
  • Inheritance and Compositions
  • Static and Class Methods
  • Bound and Unbound Methods
  • Operator Overloading
  • Polymorphism
  • Default Exception Handler
  • Catching Exceptions
  • Raise an exception
  • User defined exception
  • Decorators
  • Generators
  • Iterators
  • Co-routines

Django Training

  • Introduction
  • Why Django?
  • Django Principles
  • What you should already know
  • Course Overview
  • Introduction
  • Choosing your Versions
  • Installing Pip and Python on Windows
  • Demo: Windows Installation
  • Installing Pip and Python
  • Installing Django
  • Summary
  • Introduction
  • Creating a Django Project
  • Demo: Creating a Django Project
  • The Model-Template-View Pattern
  • Demo: Hello, World!+ Mapping URLs
  • Demo: URL Mapping+ Django Views+ Demo: Templates
  • Summary
  • Introduction+ Demo: Adding Models
  • Django Model Classes
  • Manage.py Database Commands
  • Demo: The Admin Interface
  • The Django Admin Interface
  • Demo: The Model API
  • Save and Delete
  • The Model API
  • Database Relations+ Summary
  • Introduction
  • Demo: Adding Login and Logout Views
  • More about URL Mappings
  • Demo: A Template for the Home Page
  • Authorization with Django
  • More about Django Templates
  • Demo: Adding the Home View
  • URL Mappings for Apps
  • Demo: Template Inheritance
  • Template Inheritance
  • Demo: Login Required
  • Demo: Showing Game Data on the Home Page
  • Demo: A Custom Manager Class
  • The Template Context
  • Templates: For and Include Tags
  • Summary
  • Introduction
  • Demo: Adding a HTML Form
  • Using Django Forms+ Demo: Adding Styling to the Form with Crispy-Forms+ Demo: Field Options
  • Field Options
  • Demo: Showing Invitations in a List
  • Demo: Accepting Invitations
  • Demo: Named Groups
  • Named Groups in URLs
  • Summary
  • Introduction
  • Class-based Views
  • Demo: Class-based Views
  • Demo: Adding User Signup
  • Generic Views
  • Debugging Django
  • Demo: The Django Debug Toolbar
  • Resources

Machine Learning with Python

  • NLTK
  • Scikit-Learn
  • MatplotLib
  • Pandas
  • Numpy
  • Row representation
  • Column Representation
  • Vector Spaces
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Naive Bayes
  • Support Vector Machines
  • K-means clustering
  • Fourier Transform
  • Wavelet Transform
  • Bag Of words
  • TF-IDF
  • Second Order Norm.
  • Cosine similarity.
  • Correlation co-efficient
  • Principal Component Analysis
  • Singular Value Decomposition
  • Convex Optimization

Data Science with Python

2.1 Invoking the Interpreter

  • 2.1.1 Argument Passing
  • 2.1.2 Intractive Mode

2.2 The Interpreter and its Environment

  • 2.2.1 Source Code Encoding

3.1 Using Python as a calculator

  • 3.1.1 Numbers
  • 3.1.2 Strings
  • 3.1.3 Unicode Strings
  • 3.1.4 Lists

3.2 first step towards programming

4.1 if Statement

4.2 for Statement

4.3 The range() Function

4.4 break and continue statements and else clauses on loops

4.5 pass statements

4.6 defining statements

4.7 more defining statements

  • 4.7.1 Default Argument Values
  • 4.7.2 Keyword Argument
  • 4.7.3 Arbitary Argument Lists
  • 4.7.4 Unpacking Argument Lists
  • 4.7.5 Lambda Expressions
  • 4.7.6 Documentation Settings

4.8 Intermezzo : Coding Style

5.1 More on Lists

  • 5.1.1 Using Lists as Stacks
  • 5.1.2 Using USN as Queues
  • 5.1.3. Functional Programming Tools
  • 5.1.4 list Comprehensions
  • 5.1.5Nested List Comprehensions

5.2 The del statement

5.3 Tupfes and Sequences

5.4 Sets

5.5 Dictionaries

5.6 Looping Techniques

5.7 More on Conditions

5.8 Comparing Sequences and Other Types

6.1 More on Modules

  • 6.1.1 Executing modules as scripts
  • 6.1.2 The Module Search Path
  • 6.1.3 ‘Compiled’ Python files

6.2 Standard Modules

6.3 The dir() Function

6.4 Packages

  • 6.4.1 Importing • From a Package
  • 6.4.2 Intra-package References
  • 6.4.3 Packages in Multiple Directories

7.1 Fancier Output Formatting

  • 7.1 A . Old string formatting

7.2. Reading and Writing Files

  • 7.2.1. Methods of File Objects
  • 7.2.2. Saving structured data with json
  • 8.1 Syntax Errors
  • 8.2 Exceptions
  • 8.3 Handling Exceptions
  • 8.4 Raising Exceptions
  • 8.5 User-defined Exceptions
  • 8.6 Defining Clean-up Actions
  • 8.7 Predefined Clean-up Actions

9.1. A Word About Names and Objects

9.2. Python Scopes and Namespaces

9.3. A First Look at Classes

  • 3.1. Class Definition Syntax
  • 3.2. Class Objects
  • 3.3. Instance Objects
  • 3.4. Method Objects
  • 3.5. Class and Instance Variables

9.4. Random Remarks

9.5. Inheritance

  • 5.1. Multiple Inheritance

9.6. Private Variables and Class-local References

9.7. Odds and Ends

9.8. Exceptions Are Classes Too

9.9. Iterators

9.10. Generators

9.11. Generator Expressions

  • Numpy
  • 2D Numpy Array
  • Basic Statistics with Numpy
  • Basic plot with matplotlib
  • Histograms
  • Customization
  • Boolean logic and control Flow
  • Pandas
  • We will work on 100 + programs in this session
  • Other than the above mentioned topic we will cover GUI Development
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