Syllabus Python For Machine Learning - CST 283 - KTU Minor

 SYLLABUS

Module I

Programming Environment and Python Basics:

Getting Started with Python Programming - Running code in the interactive shell, Editing, Saving, and Running a script. Using editors - IDLE, Jupyter. The software development process - Case Study.

Basic coding skills - Working with data types, Numeric data types and Character sets, Keywords,Variables and Assignment statement, Operators, Expressions, Working with numeric data, Type conversions, Comments in the program. Input, Processing, and Output. Formatting output. How Python works. Detecting and correcting syntax errors. Using built in functions and modules in math module.

Module II

Building Python Programs:

Control statements - Selection structure (if-else, switch-case), Iteration structure(for, while),Testing the control statements, Lazy evaluation. Functions - Hiding redundancy and complexity,Arguments and return values, Variable scopes and parameter passing, Named arguments, Main function, Working with recursion, Lambda functions. Strings and number systems – String function, Handling numbers in various formats.

Module III

Data Representation:

Lists - Basic list Operations and functions, List of lists, Slicing, Searching and sorting list, List comprehension. Work with tuples. Sets. Work with dates and times. Dictionaries – Dictionary

Module IV

Object Oriented Programming:

Design with classes - Objects and Classes, Methods, Instance Variables, Constructor, Accessors and Mutators. Structuring classes with Inheritance and Polymorphism. Abstract Classes.Exceptions - Handle a single exception, handle multiple exceptions.

Module V

Data Processing:

The os and sys modules. Introduction to file I/O - Reading and writing text files, Manipulating binary files. NumPy - Basics, Creating arrays, Arithmetic, Slicing, Matrix Operations, Random

numbers. Plotting and visualization. Matplotlib - Basic plot, Ticks, Labels, and Legends.

Working with CSV files. – Pandas - Reading, Manipulating, and Processing Data.

Text Books:

1.Kenneth A Lambert., Fundamentals of Python : First Programs, 2/e, Cengage Publishing,

2016

2.Wes McKinney, Python for Data Analysis, 2/e, Shroff / O’Reilly Publishers, 2017

Reference Books:

1. Allen B. Downey, Think Python: How to Think Like a Computer Scientist, 2/e, Schroff,

2016

2. Michael Urban and Joel Murach, Python Programming, Shroff/Murach, 2016

3. David M.Baezly, Python Essential Reference. Addison-Wesley Professional; 4/e, 2009.

4. Charles Severance. Python for Informatics: Exploring Information,

5. http://swcarpentry.github.io/python-novice-gapminder/

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