Python Course Detail

Course Overview

This course serves as an introduction to programming using Python, covering basic to intermediate concepts. Students will learn the fundamentals of Python syntax, data types, control structures, functions, and object-oriented programming. Practical exercises and projects will reinforce learning and provide hands-on experience with real-world applications of Python.

Preface to Python programming language

1. Preface to Python :

• Overview of python
• Need for programming
• Advantages of programming

2. Features of python

3. Operation of python

4. Integrated development terrain

5. Preface to python variables

6. Preface to Data types

7. Preface to python drivers and Strings

8. Python Programs :

• Associations using python
• Python operations in colorful disciplines
• Variables
• Operands and expressions
• tentative statements
• circles
• Structural pattern matching

• preface to Python and IDEs– Abecedarian generalities of the Python programming language and the application of different IDEs for Python development, similar as Jupyter, Pycharm,etc.
• Python Basics– Variables, Data Types, circles, tentative Statements, Functions, Decorators, Lambda Functions, train running, Exception running,etc.
• Object acquainted Programming– Overview of OOP principles including classes, objects, heritage, abstraction, polymorphism, encapsulation,etc.
• Hands- on Sessions and Assignments for Practice– Practical operation of the forenamed generalities through real- world problem scripts for enhanced appreciation Data Handling with NumPy
• NumPy Arrays, CRUD Operations,etc.
• Linear Algebra– Matrix addition, smut operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, and Matrices Data Manipulation Using Pandas
• Loading the data, data frames, series, smut operations, unyoking the data, etc. DataPre-processing
• Exploratory Data Analysis, point engineering, point scaling, Normalization, standardization,etc.
• Null Value insinuations, Outliers Analysis and Handling, VIF, Bias- friction trade- off,cross-validation ways, train- test split,etc.

Data Visualization

• Bar maps, smatter plots, count plots, line plots, pie maps, donut maps,etc. with Python matplotlib
• Retrogression plots, categorical plots, area plots, etc. with Python ocean born

Sequences and Fil.

Functions and Object- acquainted Programming

Working with Modules and Handling Exceptions

Array Manipulation using NumPy

Data Manipulation using Pandas

Data Visualization using Matplotlib and Seaborn

GUI Programming

Developing Web Charts and Representing Information using Plots (tone- paced)

Web Scraping and Computer Vision using OpenCV( Self- Paced)

Database Integration with Python

• Stylish Python libraries
• Pandas
• Matplotlib
• NumPy
• Tensor Flow

Machine learning is a field of study within computer wisdom that involves the use of algorithms to pretend mortal literacy processes. These algorithms are trained using statistical styles and are able of making prognostications. As the algorithms continue to learn, their vaticination delicacy improves over time.

• Module 1 – preface to Machine Learning
• Module 2 – Supervised Learning and Linear Regression
• Module 3 – Bracket and Logistic Retrogression
• Module 4 – Unsupervised literacy
• Module 5 – preface to Deep Learning
• Module 6 – Time Series Analysis
• Python with Data Science
• Software Engineering for Data Science
• Artificial Intelligence and Deep Learning with Tensor Flow
• Natural Language Processing
• Working with Large Datasets

Capstone Project

• Individual or group project applying machine learning techniques to a real-world dataset
• Project presentation and report