Python-Getting Started Training

Duration : 3 Days

Date : 22, 23, 24 May 2023

Overview

The training program gets participants started with the basics of python and ensures that they have a good understanding of all popular and standard concepts needed to start working on Python immediately. The participants undergoing the course will have a good understanding of – Basic Python Programming and its various constructs, commonly used Python libraries, Input & Outputs, Error Handling, etc

Prerequisite for Python

Good proficiency in other programming langaugs like C/C++/Java/PHP etc

This covers Basic Python concepts that can be covered in 3 days. This covers Python data types, operators, conditions, for, while loop, functions, seq and non sequential objects, reading and writing to file command line.

Course Contents

Python Background

• Background on Python
• Introduction to Machine Learning
• Why Python is used in Machine Learning?
• Details of Python 1, 2, 3
• Structuring with Indentation
• 2 Modes: Normal and Interactive (Shell)
  • Why Python is so popular for ML
  • Python Success Stories – Apps in Python
  • Python Basics: indent, blocks, CMD
  • Dynamic typing and strongly typed

Tools/IDE Intro

• Demo on CMD
• Jupyter Intro/Pycharm IDE

Structure and Type

• Basic Structure of Python program
• Jupyter Tutorial
• In class Demo on Python
• Primary Data types – int, float, …
• Demo on size of int, float
• Demo on casting
• Demo on complex
• Coding tasks

Python Operators & Conditional Statements

• Operators
• Operators and their precedence
• Arithmetic, comparison, membership, assignment, Logical, bitwise, identity
• id (Understand how objects are stored in memory)
• Demo on variables and address
• Conditional Statements
• Membership
• In class Demo in and is
• In class Demo Operators, priority, conditional
• Demo on bitwise
• Coding Tasks

Loops

• While Loops
• For Loops
• How for loop in python differs from Java
• For loop written in While using iterable *
• Coding Tasks

Functions

• Functions and different types of functions
• Calling protocol for mutable and immutable
• Positional, keyword, default, dynamic, lambda
• Demo on lambda Function
• Assignment on Pub entry validator

Print and Reading Writing from Terminal

• Output with Print
• Formatted output
• input and raw_input via the keyboard
• Reading and writing from terminals
• Demo on Reading and Writing from Terminal
• Assignment on Reading from terminal

Sequential and Non Sequential DT

• Sequential Data Types – String, List, Tuple,
• byte_sequences, byte arrays, range object
• Common functions across all sequential types
• [], +, +=, *, *=, len, in, count, slicing
• Mutable and Immutable

String

• String Operations
• Access, +,*, slice
• split, join
• Other string functions
• Demo on String and its functions
• Demo on String slice
• Demo on String Format

Lists

• List
• List type explained
• No arrays in basic python
• Contents of list
• List inside a list, tuple inside a list, set, dict
• Slicing works in list also
• Functions in list – Creation,….
• Demo on list slice
• Assignment on List access
• Assignment on List slice and duplicates

Tuple

• Tuple
• Tuple Type explained – immutable
• Functions
• Can a tuple contain a list
• Why tuple
• Demo on Tuple
• Demo on Tuple and address
• Demo on Tuple immutability
• Coding Tasks

Byte, Byte Array, Range

• Byte, Byte Sequence Byte Array
• range object
• Demo on Byte, Byte Sequence, Range

Non Sequential

• Non Sequential Dictionaries, Sets

Dictionary

• Dictionaries: Concepts, and why
• Key Value concept
• simple eg of dict
• iterators -keys, values, both
• check for keys using in
• check for values using in
• Functions in dict
• what can come as key? Any immutable object
• What can come as values: Any mutable or immutable object
• Demo on dict creation, iteration and its functions
• Demo: Can dicts be indexed even though it is non-sequential?
• Coding Tasks on dict

Set

• Set Concept
• Mutable
• Functions in Set
• Immutable
• Demo on set and its functions
• Coding Tasks on set – Build a Probability based Sentiment analyzer

Modular Programming

• Modular Programming and Modules
• Intro to modules, packages, Numpy intro, Sklearn Intro (* Depending on time)

Persistence

• Pickle – Brief intro (Details in Advanced)