Introduction:

History

Difference between Programming and scripting

Features

Installation of Python on Windows and UNIX machines

Setting up path

Simple hello world program

Variables

Data types

Data structures (List, tuple, dictionaries, sets and arrays)

 Operations on strings

Operators

Input and output statements:

Print

Raw_input and input (for both 2.x and 3.x)

Conditional Statements:

If

If-else

Nested if-else

Loops and control statements:

For

While

Nested loops

Break

Continue

Pass

Functions

Defining a function

Calling a function

Types of functions

Function Arguments

Scope of the variable (global and local)

Modules:

Definition

Creating modules

Importing modules

Usage of modules(os, sys, subprocess, random json, yaml)

Reading xml files

Exception Handling:

Definition

Exception Handling ( try except and try finally)

Regular expressions:

Match

Search

Find

Findall

Finditer

Replace

Split

Oops concepts:

Class and object

Inheritance

Data hiding

Data abstraction

Working with Database and OS(Windows and Unix):

Connecting to Data base

Executing DB queries

Executing tasks of Unix and winnows using python

 

• Linear algebra: Simple linear algebra.
• Programming: I learnt programming at the same time as I was teaching myself ML, so programming skills can start from zero to proficient.
• Calculus: Some differential calculus.

Alright. We have a handle on Python programming and understand a bit about machine learning. Beyond Python there are a number of open source libraries generally used to facilitate practical machine learning. In general, these are the main so-called scientific Python libraries we put to use when performing elementary machine learning tasks (there is clearly subjectivity in this)