Machine Learning ~ Section 01 : Introduction & Supervised / Unsupervised Learning 4
- Lecture1.1
  
  Machine Learning and its Uses and Roles 10 min
- Lecture1.2
  
  1.1 Introduction 07 min
- Lecture1.3
  
  1.2 Types of Machine Learning 05 min
- Lecture1.4
  
  1.3 Different Supervised Learning Algorithms 06 min
Python ~ Section 01 : Introduction And Getting The Right Tools! 1
- Lecture2.1
  
  1.1 Introduction And Installation 05 min
Python ~ Section 02 : Basic I/O, Operators & Using IDE 5
- Lecture3.1
  
  2.1 Numbers and Strings 07 min
- Lecture3.2
  
  2.2 Lists and Dictionaries 07 min
- Lecture3.3
  
  2.3 Assignment Operators 05 min
- Lecture3.4
  
  2.4 Development Environment 04 min
- Lecture3.5
  
  2.5 Visual Studio Code: [VS_Code] 07 min
Python ~ Section 03 : Conditional Statements & Looping ! 5
- Lecture4.1
  
  3.1 Conditional Statements 05 min
- Lecture4.2
  
  3.2 User Input 05 min
- Lecture4.3
  
  3.3 WHILE Loop 05 min
- Lecture4.4
  
  3.4 FOR Loop 03 min
- Lecture4.5
  
  3.5 FOR Loop: (Dictionary Enumeration) 05 min
Python ~ Section 04 : OOPS! Functions, Classes & Exception Handling 4
- Lecture5.1
  
  4.1 Functions 07 min
- Lecture5.2
  
  4.2 Class and Objects 04 min
- Lecture5.3
  
  4.3 Constructors 05 min
- Lecture5.4
  
  4.4 Exception handling 07 min
Python ~ Section 05 : Python Modules & Experiencing Jupyter ! 5
- Lecture6.1
  
  5.1 Modules 06 min
- Lecture6.2
  
  5.2 Statistics Module 04 min
- Lecture6.3
  
  5.3 CSV Module 08 min
- Lecture6.4
  
  5.4 PIP 04 min
- Lecture6.5
  
  5.5 Jupyter Note Book 07 min
Python ~ Section 06 : Tkinter, SQL in Python & File Management 3
- Lecture7.1
  
  6.1 SQLite 10 min
- Lecture7.2
  
  6.2 Tkinter 11 min
- Lecture7.3
  
  6.3 Making [.exe] in Python 08 min
[Bonus] Python Hands-On Projects + Source Code 4
- Lecture8.1
  
  Rock Paper Scissor Python Game 13 min
- Lecture8.2
  
  Message Encode Decode in Python Project 15 min
- Lecture8.3
  
  Calculator in Python 25 min
- Lecture8.4
  
  Source Code of all 3 Projects
Python Assignments 2
- Lecture9.1
  
  Assignment No. 1
- Lecture9.2
  
  Assignment No. 2
Machine Learning ~ Section 02 : Regression [Models, Implementation] 13
- Lecture10.1
  
  2.1 Linear Regression + Introduction 10 min
- Lecture10.2
  
  2.2 Linear Regression Mathematics 13 min
- Lecture10.3
  
  2.3 Linear Regression Implementation 13 min
- Lecture10.4
  
  2.4 Regression Using Karl Pearsons Coefficient 05 min
- Lecture10.5
  
  2.5 Linear Regression using Karlpearson Coefficient Implementation 06 min
- Lecture10.6
  
  2.6 Linear Regression Library Implementation 05 min
- Lecture10.7
  
  2.7 Loss Analysis Using Mse 08 min
- Lecture10.8
  
  2.8 Mean Squared Error (MSE) 04 min
- Lecture10.9
  
  2.9 Goodness Of Fit 10 min
- Lecture10.10
  
  2.10 R-Squared Implementation 03 min
- Lecture10.11
  
  2.11 R Squared Using Karl Pearson Coefficient 05 min
- Lecture10.12
  
  2.12 R Squared Using Karl Pearson 05 min
- Lecture10.13
  
  2.13 Library Implementation of Metrics 03 min
Machine Learning ~ Section 03 : Data Processing & Pandas 4
- Lecture11.1
  
  3.1 Loss Optimizer 09 min
- Lecture11.2
  
  3.2 Gradient Descent Implementation 09 min
- Lecture11.3
  
  3.3 Data Processing Using Pandas 11 min
- Lecture11.4
  
  3.4 Train Test Split 06 min
Machine Learning ~ Section 04 : Classification, Score Analysis & [Bonus] Google Colabration 9
- Lecture12.1
  
  4.1 Classification Models 08 min
- Lecture12.2
  
  4.2 Logistic Regression 05 min
- Lecture12.3
  
  4.3 Loss For Classification Models 03 min
- Lecture12.4
  
  4.4 Log Loss Implementation 07 min
- Lecture12.5
  
  4.5 Score Analysis Basics 05 min
- Lecture12.6
  
  4.6 Confusion Matrix Implementation 08 min
- Lecture12.7
  
  4.7 Precision and Recall 07 min
- Lecture12.8
  
  4.8 F1 Score 05 min
- Lecture12.9
  
  [Bonus] Google Collaboration 09 min
Machine Learning ~ Section 05 : K-Means Clustering, Decision Tree Classifier, Support Vector Information 6
- Lecture13.1
  
  5.1 K Nearest neighbors 08 min
- Lecture13.2
  
  5.2 Iris 10 min
- Lecture13.3
  
  5.3 Support Vector Machine 10 min
- Lecture13.4
  
  5.4 Decision Tree Classifier 06 min
- Lecture13.5
  
  5.5 Digit Classification 08 min
- Lecture13.6
  
  5.6 K Means Clustering 13 min

2.3 Linear Regression Implementation

Linear Regression Implementation

Linear Regression Implementation Algorithm is a machine learning algorithm based on supervised learning. Linear regression is a part of regression analysis. Regression analysis is a technique of predictive modelling that helps you to find out the relationship between Input and the target variable. The ultimate goal of the regression algorithm is to plot a best-fit line or a curve between the data. The three main metrics that are used for evaluating the trained regression model are variance, bias and error.

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Prev 2.2 Linear Regression Mathematics

Next 2.4 Regression Using Karl Pearsons Coefficient

Machine Learning ~ Section 01 : Introduction & Supervised / Unsupervised Learning 4

Python ~ Section 01 : Introduction And Getting The Right Tools! 1

Python ~ Section 02 : Basic I/O, Operators & Using IDE 5

Python ~ Section 03 : Conditional Statements & Looping ! 5

Python ~ Section 04 : OOPS! Functions, Classes & Exception Handling 4

Python ~ Section 05 : Python Modules & Experiencing Jupyter ! 5

Python ~ Section 06 : Tkinter, SQL in Python & File Management 3

[Bonus] Python Hands-On Projects + Source Code 4

Python Assignments 2

Machine Learning ~ Section 02 : Regression [Models, Implementation] 13

Machine Learning ~ Section 03 : Data Processing & Pandas 4

Machine Learning ~ Section 04 : Classification, Score Analysis & [Bonus] Google Colabration 9

Machine Learning ~ Section 05 : K-Means Clustering, Decision Tree Classifier, Support Vector Information 6

2.3 Linear Regression Implementation

Linear Regression Implementation

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