Machine-Learning-Using-Python-VNIEW(12-07-2021)

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 120/150 minutes should attend in 150/180 minutes session with same login format

Check your attendance Here

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Day5-MLIntro

1. Ml Introduction
2. ML Types
3. Ml Use cases
4. Difference b/w ML AI,DL
5. ML life cycle
6. Types of variables in stastics

[Day5-_Notebook_Link]

[DayNo_Recorded_Video_Link]

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Day6-Simple Linear Regresiion

• Diffent types of machine learning algorithems
• Simple Linear Regression
  • Intution
  • statistical Formula's
  • Evolution Metrics

[Day6-_Notebook_Link]

[DayNo_Recorded_Video_Link]

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Day7-Simple Linear Regresiion(19-07-2021)

• Linear Regression with Multiple variables
  • Linear Equation
  • Preprocessing data
  • Evalution Metrics for LR
• Non-Linear Regression
• Polynomial Regression
  • formula
    • y=ax^2+bx^1+cx^0+c+e
  • Transforming normal features into polynomail features with degree
  • Finfinding the evalution metrics of model

[Day7-_Notebook_Link]

Day8-Regularization(20-07-2021)

• what us over fitting
• what is under fitting
• How to handle that over fitting and under fitting problems in ML
• Bias,Variance
• Calculations behind the L1 & L2 Regularisation's

[Day8 -note book]