Single Courses

Course Syllabus

Fixed number of keywords, including a set of control primitives, such as if, for, while, switch and do while.

• Section 1: Introduction to Python

  • 1. History & need of Python
  • 2. Application of Python
  • 3. Advantages of Python
  • 4. Disadvantages of Python
  • 5. Installing Python
  • 6. Program Structure
  • 7. Interactive Shell
  • 8. Executable or script files
  • 9. User Interface or IDE

• Section 2: Python Fundamentals

  • 1. Working with Interactive Mode
  • 2. Working with Script Mode
  • 3. Python Character Set
  • 4. Python Tokens, Keywords, Identifiers, Literals, Operators
  • 5. Variables and Assignments
  • 6. Input and Output in Python

• Section 3: Data Handling

  • 1. Data Types
  • 2. Numbers
  • 3. Strings
  • 4. Lists
  • 5. Tuples
  • 6. Dictionary
  • 7. Set
  • 8. Frozenset
  • 9. Bool
  • 10. Mutable and Immutable

• Section 4: String Manipulation

  • 1. Introduction to Python String
  • 2. Accessing Individual Elements
  • 3. String Operators
  • 4. String Slices
  • 5. String Functions and Methodst

• Section 5: List Manipulation

  • 1. Introduction to Python List
  • 2. Creating List
  • 3. Accessing List
  • 4. Joining List
  • 5. Replicating List
  • 6. List Slicing

• Section 6:Tuples

  • 1. Introduction to Tuple
  • 2. Creating Tuples
  • 3. Accessing Tuples
  • 4. Joining Tuples
  • 5. Replicating Tuples
  • 6. Tuple Slicing

• Section 7: Dictionaries

  • 1. Introduction to Dictionary
  • 2. Accessing values in dictionaries
  • 3. Working with dictionaries
  • 4. Properties

• Section 8: Set and Frozenset

  • 1. Introduction to Set & Frozenset
  • 2. Creating Set and Frozenset
  • 3. Accessing and Joining
  • 4. Replicating and Slicing

• Section 9:Operators

  • 1. Arithmetic Operators
  • 2. Relational Operators
  • 3. Logical Operators
  • 4. Membership Operators
  • 5. Identity Operators
  • 6. Bitwise Operators
  • 7. Assignment Operators
  • 8. Operators Precedence
  • 9. Evaluating Expression
  • 10. Type Casting

• Section 10: Program Control Flow

  • 1. Conditional Statements
  • 2. The if Statement
  • 3. The if-else Statement
  • 4. The if-elif Statement
  • 5. Nested if Statements
  • 6. Python Indentation
  • 7. Looping and Iteration
  • 8. The For Loop
  • 9. The While Loop
  • 10. Loop else Statement
  • 11. Nested Loops
  • 12. Break and Continue
  • 13. The Range Function
  • 14. Introduction to range()p
  • 15. Types of range() function
  • 16. Use of range() function

• Section 11: Introduction to Functions

  • 1. Built-In Functions
  • 2. Introduction to Functions
  • 3. Using a Functions
  • 4. Python Function Types
  • 5. Python Function Structure
  • 6. E.g. - map, zip, reduce, filter,any, chr, ord, sorted, globals, locals, all, etc.
  • 7. User Defined Functions
  • 8. Structure of a Python Program w.r.t. UDF
  • 9. Types of Functions
  • 10. Invoking UDF
  • 11. Flow of Execution
  • 12. Arguments and Parameters
  • 13. Default Arguments, Named Arguments
  • 14. Scope of Variables
  • 15. Lambda function
  • 16. Recursion Function
  • 17. Use of recursion function

• Section 12: Modules and Packages

  • 1. Built-in Modules
  • 2. Importing Modules in Python Programs
  • 3. Random Modules Working
  • 4. E.g. - builtins, os, time, datetime, calendar, sys, etc.
  • 5. User Defined Functions
  • 6. Structure of Python Modules

• Section 13: File Operations

  • 1. Text and Bytes files
  • 2. Opening a file
  • 3. Reading and Writing Files
  • 4. Other File tools
  • 5. MS Excel files
  • 6. Intro to MS Excel files

• Section 14: Classes or Objects

  • 1. User Defined Data Type Classes
  • 2. Objects as Instances of Classes
  • 3. Creating Class and Objects
  • 4. Creating Objects by Passing Values
  • 5. Variables & Methods in a Class

• Section 15: Exception Handling

  • 1. Default Exception and Errors
  • 2. Catching Exceptions
  • 3. Raise an exception
  • 4. Try.... except statement
  • 5. Raise, Assert, Finally blocks
  • 6. User defined exception

• Section 16: Introduction to OOPS

  • 1. Procedural Vs Modular Programming
  • 2. The Object Oriented Programming
  • 3. Data Abstraction
  • 4. Data Hiding
  • 5. Encapsulation
  • 6. Modularity
  • 7. Inheritance
  • 8. Polymorphism

• Section 17: Database

  • 1. Introduction to MySQL
  • 2. PYMYSQL Connections
  • 3. Executing queries
  • 4. Transactions
  • 5. Handling error

• Section 18: GUI Programming

  • 1. Introduction
  • 2. Tkinter programming
  • 3. Tkinter widgets
  • 4. Frame
  • 5. Button
  • 6. Label
  • 7. Entry

• Section 19: Turtle Programming

  • 1. Introduction to Turtle
  • 2. Controlling Turtle
  • 3. Animation Programming

• Section 20: Multithreading

  • 1. Thread and Process
  • 2. Starting a thread
  • 3. Threading module
  • 4. Synchronizing threads
  • 5. Multithreaded Priority Queue

• Section 21: Networking

  • 1. Socket Module
  • 2. Server-client-socket
  • 3. Connecting client server
  • 4. Client-server chatting program

• Section 22: Regular Expressions

  • 1. Match function
  • 2. Search function
  • 3. Grouping
  • 4. Matching at Beginning
  • 5. Matching at End
  • 6. Match Objects
  • 7. Flags

• Section 23: Advanced Concepts

  • 1. Decorators
  • 2. Generators
  • 3. Iterators
  • 4. Co-routines

• Section 24: CGI

  • 1. Architecture
  • 2. CGI environment variable
  • 3. GET and POST methods
  • 4. Cookies
  • 5. File upload

• Section 25: Projects

  • 1. Console Project
  • 2. GUI Project

• Section 26: Introduction to Machine Learning

  • 1. What is ML? Why ML?
  • 2. Python Libraries for ML
  • 3. Types of ML –
  • 4. Intro to Supervised ML
  • 5. Introduction to Unsupervised ML
  • 6. Introduction to Reinforcement ML
  • 7. ML Mathematical Background
  • 8. Matrix Ops Probability Theory (Bayes Theorem)t
  • 9. Statistical knowledge for ML-
  • 10. Mean
  • 11. Median
  • 12. Mode
  • 13. Z-scores
  • 14. Bias-variance dichotomy
  • 15. Development Tools
  • 16. Anaconda
  • 17. Jupyter NB

• Section 27: ML Libraries Explained

  • 1. Scipy
  • 2. Numpy
  • 3. Matplotlib
  • 4. Pandas
  • 5. Seaborn
  • 6. Scikit-learn
  • 7. ML Glossary- Variables types, k-fold, CV, AUS
  • 8. F1 score, Overfitting/Underfitting, Generalization
  • 9. Data Split & hyper parameter training
  • 10. Data wrangling using Pandas
  • 11. Pre-processing data and feature engineering
  • 12. Exploratory Data Analysis using Visualization
  • 13. Scikit-learn Library for ML
  • 14. Code Exercises

• Section 28: Supervised Learning

  • 1. Regression:
  • 2. Different types of Regression
  • 3. Linear Regression
  • 4. Logistic Regression
  • 5. Decision tree Algorithms
  • 6. Real-world code exercises

• Section 29: Supervised Learning Classification

  • 1. Naïve-Bayes Classification
  • 2. KNN Classification
  • 3. Real-world code exercises

• Section 30: Unsupervised Learning

  • 1. Clustering Introduction
  • 2. k-means clustering
  • 3. Code Exercises

• Section 31:Advanced Topics

  • 1. Curse of Dimensionality-PCA Algorithm
  • 2. SVM Classification

• Section 32: Introducation to Open CV

  • 1. Core functionality of OPEN CV
  • 2. Image Processing
  • 3. GUI and Media Interfacing
  • 4. Image Input and Output
  • 5. Video Input and Output

• Section 33: Real-Time Mini Projects

  • 1. Students are required to have the following prerequisites:
  • 2. Python Programming
  • 3. Basic Statistics
  • 4. Basic Calculus
  • 5. Basic Linear Algebra