Machine-Learning-Using-Python

This repository consists of all the files, resources, and recorded session links which are discussed during Machine Learning using Python Online Training.

APSSDC-ML-Datasets → [Click Here]

Few resources avaliable @ [resources.md] file don't forget to use them

Instructions for attendance

Everyone should compulsory follow the below instruction in order to get the attendance --> Certificate

1. Login format rollnumber-name-college
2. Don't give spaces in roll number or shorcut of your roll number
3. Don't give spaces between rollnumber and name (only - single minus or hyphen character)
4. Make sure roll number should match with the registered roll number
5. Minimum 120 minutes to attend in 150 minutes session

Attendance sheet reference purpose only(make sure to follow above instructions to get present) → [clickHere]

Your details printed on Certificates verify once → [clickHere]

Day1 Introduction to Machine Learning (05/Oct/2020)

Discussed Concepts:

1. What is machine Learning
2. Types of ML
3. Applications
4. Algorithms

• ML pdf
• Anaconda installation pdf
• Notebook
• Notes.txt
• Employee.csv
• Recorded Video

Day2 Prediction of RIL revenue by Linear Regression (06/oct/2020)

Discussed Concepts

1. Linear Regression for

• Prediction of Salary

• Prediction Reliance Industry revenue with Jio subscribers

• Notebook

• Salary_Data.csv

• Reliance.csv

• Recorded Video

Day3 Multi Linear Regression and Polynomial Features (07/Oct/2020)

Discussed Concepts

1. Multi Linear Regression for house price prediction of boston dataset
2. Applying Polynomial Features for Salary prediction dataset

• Notebook
• Recorded video
• image1
• image2
• image3

Day4 KNN algorithm (08/Oct/2020)

Discussed Concepts

1. K-Nearest Neighbour Algorithm

• Notebook
• shirt.csv
• Recorded Video

Day5 Classification Algorithms (09/Oct/2020)

Discussed Concepts

1. Logistic Regression Algorithm
2. Support Vector Machine

• Notebook-1
• Notebook-2
• Recorded Video

Day6 Decision Tree Algorithms (10/Oct/2020)

Discussed Concepts

1. Decision Tree

• Notebook 1
• Notebook 2
• Recorded Video

Day7 Random Forest Algorithms (12/Oct/2020)

Discussed Concepts

1. Random Forest Algorithm

• Notebook
• Recorded Video

Day8 Unsupervised Learning (13/Oct/2020)

Discussed Concepts

1. K-Means Algorithm

• Notebook
• Recorded Video

Day9 Principal Component Analysis (14/Oct/2020)

Discussed Concepts

1. Principal Component Analysis
2. Saving model to pickle file
3. connecting ML model with Flask application

• Notebook
• Notebook2
• fruits.pkl
• Fruits_dataset
• Recorded Video