Training & Duration
- Live classes (Monday to Friday)
- 4 Weeks of Training & 2 Weeks of Project Work
Course Features
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- Online lectures: Online live lectures.
- Updated Quality content: Content is the latest and gets updated regularly to meet the current industry demands.
Test & Evaluation
1. During the program, the participants will have to take all the assignments given to them for better learning.
2. At the end of the program, a final assessment will be conducted.
Certification
1. All successful participants will be provided with a certificate of completion.
2. Students who do not complete the course / leave it midway will not be awarded any certificate.
Basic knowledge of any programming language will be helpful
Course Outline
1: Introduction
- What is Python..?
- Why Should I learn Python..?
- Installing Python
- How to execute Python program
- Write your first program
2: Variables & Data Types
- Variables
- Numbers
- String
- Lists ,Tuples & Dictionary
3: Conditional Statements & Loops
- if...statement
- if...else statement
- elif...statement
- The while...Loop
- The for....Loop
4: Control Statements
- continue statement
- break statement
- pass statement
5: Functions
- Define function
- Calling a function
- Function arguments
- Built-in functions
6: Modules & Packages
- Modules
- How to import a module...?
- Command line arguments
- Packages
- Creating custom packages
7: Classes & Objects
- Introduction about classes & objects
- Creating a class & object
- Inheritance
- Methods Overriding
8: Files & Directories
- Writing data to a file
- Reading data from a file
- Working with csv file
- The os module
- Working with files and directories
9: Introduction to Sqlite database
- Overview
- Create Database
- Create Table
- Drop Table
- Insert query
- Select query
- Delete and Update query
- WHERE, AND & OR Clause
Some Basics of Machine Learning
10: Introduction to Machine learning
- What is ML?
- Examples on ML
- Types of ML
- Introduction to the basic terminology
- ML package :scikit-learn
- Anaconda
- How to install anaconda
11: Regression
- Introduction
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Evaluate Performance of a linear regression model
- Overfitting and underfitting
12: Logistic Regression
- Logistic Regression theory
- Implementing Logistic regression with scikit-learn
- Logistic Regression Parameters
- Multi-class classification
13: Features Extraction and Naïve Bayes Classification
- Features extraction
- Countvectorizer
- TF-IDF
- Theory Naive Bayes Algorithm
- Email Spam filtering using Naive Bayes
