Introduction to Python Programming

Python Training in Udaipur is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms. python training for MCA 6 Months Industrail Training Students In Udaipur Rajathan. Python Students Training in Udaipur All The Project Base Training and Live Project on Python Udaipur.

Over the entire course you will learn:

  • Python
  • HTML & HTML 5
  • CSS & CSS 3
  • Responsive Design with Bootstrap
  • JavaScript
  • jQuery
  • AJAX
  • MongoDB
  • APIs (both creating them and interacting with them)

Python Programming Training Outline

An Overview of Python

  • What is Python?
  • Interpreted languages
  • Advantages and disadvantages
  • Downloading and installing
  • Which version of Python
  • Where to find documentation

Running Python Scripts

  • Structure of a Python script
  • Using the interpreter interactively
  • Running standalone scripts under Unix and Windows

Getting Started

  • Using variables
  • String types: normal, raw and Unicode
  • String operators and expressions
  • Methods print(), str(), and int()
  • The format() method
  • Getting user input with the input() method
  • Asking users for input
  • Command line parameters

Flow Control

  • About flow control
  • if Statements
  • for Statements
  • while Statements
  • The range() Function
  • break and continue Statements, and else Clauses on Loops
  • pass Statements

Defining Functions

  • Syntax of function definition
  • Keyword Arguments
  • Default Argument Values
  • Keyword Arguments
  • Arbitrary Argument Lists
  • Unpacking Argument Lists
  • Lambda Expressions
  • Documentation Strings
  • Function Annotations

Data Structures

  • Using Lists as Stacks
  • Using Lists as Queues
  • List Comprehensions
  • Nested List Comprehensions
  • The del statement
  • Tuples and Sequences
  • Sets
  • Dictionaries
  • Looping Techniques


  • Executing modules as scripts
  • The Module Search Path
  • “Compiled” Python files
  • Standard Modules
  • The dir() Function
  • Packages
  • Importing * From a Package
  • Intra-package References
  • Packages in Multiple Directories

Working with Files

  • Text file I/O overview
  • Opening a text file
  • Reading text files
  • Raw (binary) data
  • Using the pickle module
  • Writing to a text file
  • Methods of File Objects
  • Saving structured data with json

Errors and Exceptions

  • Syntax Errors
  • Exceptions
  • Handling Exceptions
  • Raising Exceptions
  • User-defined Exceptions
  • Defining Clean-up Actions
  • Predefined Clean-up Actions


  • Class Definition Syntax
  • Class Objects
  • Instance Objects
  • Method Objects
  • Class and Instance Variables
  • Inheritance
  • Multiple Inheritance
  • Private Variables
  • Odds and Ends
  • Iterators
  • Generators
  • Python Scopes and Namespaces

Standard Library

  • Operating System Interface
  • File Wildcards
  • Command Line Arguments
  • String Pattern Matching
  • Mathematics
  • Internet Access
  • Dates and Times
  • Data Compression
  • Performance Measurement
  • Quality Control
  • Templating
  • Working with Binary Data Record Layouts
  • Multi-threading
  • Logging

Machine Learning

Pre Program Preparation

    Python for Data Analysis: Get acquainted with Data Strtuctures, Object Oriented Programming, Data Manipulation and Data Visualization in Python
  • Introduction to SQL: Learn SQL for querying information from databases
  • Math for Data Analysis: Brush up your knowledge of Linear Algebra, Matrices, Eigen Vectors and their application for Data Analysis

Statistics Essentails

  • Inferential Statistics: Learn Probability Distribution Functions, Random Variables, Sampling Methods, Central Limit Theorem and more to draw inferences
  • Hypothesis Testing: Understand how to formulate and test hypotheses to solve business problems Exploratory Data Analysis: Learn how to summarize data sets and derive initial insights
  • MachineLearning

    • Linear Regression: Learn to implement linear regression and predict continuous data values
    • Supervised Learning: Understand and implement algorithms like Naive Bayes and Logistic Regression
    • Unsupervised Learning: Learn how to create segments based on similarities using K-Means and Hierarchical clustering
    • Support Vector Machines: Learn how to classify data points using support vectors
    • Decision Trees: Tree-based model that is simple and easy to use. Learn the fundamentals on how to implement them

    Natural Languege Processing

    • Basics of text processing: Get started with the Natural language toolkit, learn the basics of text processing in python
    • Lexical processing: Learn how to extract features from unstructured text and build machine learning models on text data
    • Syntax and Semantics: Conduct sentiment analysis, learn to parse English sentences and extract meaning from them
    • Other problems in text analytics: Explore the applications of text analytics in new areas and various business domains
  • Information flow in a neural network: Understand the components and structure of artificial neural networks
  • Training a neural network: Learn the cutting-edge techniques used to train highly complex neural networks
  • Convolutional Neural Networks: Use CNN's to solve complex image classification problems
  • Recurrent Neural Networks: Study LSTMs and RNN's applications in text analytic
  • Creating and deploying networks using Tensorflow and keras: Build and deploy your own deep neural networks on a website, learn to use the Tensorflow API and Keras
  • Keen Infotech Python Latest News

    New PyPI launched, legacy PyPI shutting down April 30 New PyPI launched, legacy PyPI shutting down April 30. Starting today, the canonical Python Package Index is at and uses the new Warehouse codebase. We announced the beta on March 26 and your feedback and test usage have helped us get it production-ready. Monday April 16 (2018-04-16): We launched the new PyPI, redirecting browser traffic and API calls (including "pip install") from to the new site. The old codebase is still available at for now. Monday April 30 (2018-04-30): We plan to shut down legacy PyPI . The address will continue to redirect to Warehouse

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