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Python Course Content

  1. Overview

    "Python is powerful... and fast;
    plays well with others;
    runs everywhere;
    is friendly & easy to learn;
    is Open."

    Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum and first released in 1991. An interpreted language, Python has a design philosophy that emphasizes code readability (notably using whitespace indentation to delimit code blocks rather than curly brackets or keywords), and a syntax that allows programmers to express concepts in fewer lines of code than might be used in languages such as C++ or Java. It provides constructs that enable clear programming on both small and large scales.

  2. Python Course Outline

    Overview of Python. Why Python?
    • Environment Setup
    • Basic Syntax
    • Variable and Data Types
    • Operator
    • Input & Output

    Conditional Statement
    • If
    • Ifelse
    • Nested if

    Loop/Iteration
    • For
    • While
    • Nested loop
    • Break , continue

    Strings
    • Accessing Strings
    • Function and Methods

    Set
    • Properties
    • Related operations
    • Comparison with dictionary

    Tuple
    • Accessing tuples
    • Operations
    • Working
    • Functions and Methods

    Dictionary
    • Accessing values in dictionaries
    • Working with dictionaries
    • Properties

    Functions
    • Defining a function
    • Calling a function
    • Types of functions
    • Function Arguments
    • Anonymous functions

    Types Of Variables
    • Global
    • local variables

    Modules
    • Importing module
    • Math module
    • Random module
    • Packages

    File Handling
    • Printing on screen
    • Reading data from keyboard
    • Opening and closing file
    • Reading and writing files
    • Other Functions

    Exception Handling
    • Exception
    • Exception Handling
    • Except clause
    • Try ? finally clause
    • User Defined Exceptions

    OOPs Concept
    • Class and object
    • Attributes
    • Inheritance
    • Overloading
    • Overriding
    • Data hiding

    Regular expressions
    • Match function
    • Search function
    • Matching VS Searching
    • Modifiers
    • Patterns

    Database
    • Introduction
    • Connections
    • Executing queries
    • Transactions
    • Handling error

    Networking
    • Socket
    • Socket Module
    • Methods
    • Client and server
    • Internet modules

    Multithreading
    • Thread
    • Starting a thread
    • Threading module
    • Synchronizing threads
    • Multithreaded Priority Queue

    GUI Programming
    • Introduction
    • Tkinter programming
    • Tkinter widgets

    CGI
    • Introduction
    • Architecture
    • CGI environment variable
    • GET and POST methods
    • Cookies
    • File upload

    Statistics - Machine Learning Prerequisites
    • Statistics - data terminology, measurement scales, types of data
    • Libraries - IPython, Matplotlib
    • Measures, Moments, Variance, Std. Deviation using numpy
    • Distributions, Probability and Bayes’ Theorem using Scipy
    • Numpy - arrays, matrices, related operations
    • Scipy - overview, areas of application

    Numerical measure
    • Matplotlib introduction
    • Deviation and variance
    • Standard deviation
    • Covariance and correlation
    • Conditional probability

    Distribution/Probability functions
    • Installing Numpy
    • Numpy arrays and matrices
    • Installing Scipy
    • Scipy Modules and stats

    Apply Supervised Learning process flow, regression analysis
    • Apply Unsupervised Learning process flow, clustering
    • Apply Linear Regression, Multivariate Regression
    • Measure accuracy using Mean Squared Error, Cross Validation
    • Analyze data using Pandas
    • Feature engineer datasets using PCA, Bias/Variance analysis

    Apply classifications algorithms like KNN, Random Forests, SVM etc.
    • Apply clustering algorithms like K-Means, Hierarchical clustering etc.
    • Compute classification and clustering metrics to ascertain model accuracy

    Web Scraping in Python and Project Work
    • Goal : Discuss about the powerful web scraping using Python and discuss a real-world project.
    • Discuss web scraping and its advantages
    • Discuss Steps Involved in Web Scraping

    Use BeautifulSouppackage and its functions
    • Scrape IMDB webpage
    • Fetch Streaming Tweets from Twitter
    • Perform Sentiment Analysis on tweets Fetched from Twitter and determine which is more popular Ferrari or Porsche

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