-
Week 1 20
-
1.1 Introduction And Installation 05 minLecture1.1
-
1.2 Numbers and Strings 07 minLecture1.2
-
1.3 Lists and Dictionaries 07 minLecture1.3
-
1.4 Assignment Operators 05 minLecture1.4
-
2.1 Development Environment 04 minLecture1.5
-
2.2 Visual Studio Code: [VS_Code] 07 minLecture1.6
-
2.3 Conditional Statements 05 minLecture1.7
-
2.4 User Input 05 minLecture1.8
-
2.5 WHILE Loop 05 minLecture1.9
-
3.1 FOR Loop 03 minLecture1.10
-
3.2 FOR Loop: (Dictionary Enumeration) 05 minLecture1.11
-
3.3 Functions 07 minLecture1.12
-
4.1 Class and Objects 04 minLecture1.13
-
4.2 Constructors 05 minLecture1.14
-
5.1 Exception handling 07 minLecture1.15
-
5.2 Modules 06 minLecture1.16
-
5.3 Statistics Module 04 minLecture1.17
-
6.1 CSV Module 08 minLecture1.18
-
6.2 PIP 04 minLecture1.19
-
6.3 Jupyter Note Book 07 minLecture1.20
-
-
Week 2 12
-
1.1 SQLite 10 minLecture2.1
-
2.1 Introduction to ML 07 minLecture2.2
-
2.2 Types of Machine Learning 05 minLecture2.3
-
2.3 Different Supervised Learning Algorithms 06 minLecture2.4
-
3.1 Tkinter 11 minLecture2.5
-
3.2 Making [.exe] in Python 08 minLecture2.6
-
4.1 Linear Regression + Introduction 10 minLecture2.7
-
4.2 Linear Regression Mathematics 13 minLecture2.8
-
4.3 Linear Regression Implementation 13 minLecture2.9
-
5.1Rock Paper Scissor Python Game 13 minLecture2.10
-
6.1 Regression Using Karl Pearson Coefficient 05 minLecture2.11
-
6.2 Linear Regression using Karlpearson Coefficient ImplementationLecture2.12
-
-
Week 3 12
-
1.1 Message Encode Decode in Python Project 15 minLecture3.1
-
2.1 Linear Regression Library Implementation 05 minLecture3.2
-
2.2 Loss Analysis Using Mse 08 minLecture3.3
-
2.3 Mean Squared Error 04 minLecture3.4
-
3.1 Calculator in Python 25 minLecture3.5
-
4.1 Goodness Of Fit 10 minLecture3.6
-
4.2 R-Squared Implementation 03 minLecture3.7
-
5.1 R Squared Using Karl Pearson Coefficient 05 minLecture3.8
-
5.2 R-Square Dusing Kral pearson 05 minLecture3.9
-
5.3 Library Implementation of Metrics 03 minLecture3.10
-
6.1 Loss Optimizer 09 minLecture3.11
-
6.2 Gradient Descent Implementation 09 minLecture3.12
-
-
Week 4 17
-
1.1 Data Processing Using Pandas 11 minLecture4.1
-
1.2 Train Test Split 06 minLecture4.2
-
1.3 Classification Models 08 minLecture4.3
-
2.1 Logistic Regression 05 minLecture4.4
-
2.2 Loss For Classification Models 03 minLecture4.5
-
2.3 Log Loss Implementation 07 minLecture4.6
-
2.4 Score Analysis Basics 05 minLecture4.7
-
3.1 Confusion Matrix Implementation 08 minLecture4.8
-
3.2 Precision & RecallLecture4.9
-
3.3 F1 Score 05 minLecture4.10
-
4.1 Google Colabration 09 minLecture4.11
-
4.2 K Nearest neighbors 08 minLecture4.12
-
5.1 IrisLecture4.13
-
5.2 Support Vector Machine 10 minLecture4.14
-
5.3 Decision Tree Classifier 06 minLecture4.15
-
6.1 Digit Classification 08 minLecture4.16
-
6.2 K Means Clustering 13 minLecture4.17
-
-
Python Projects Source Code 1
-
Source Code of all 3 ProjectsLecture5.1
-
This content is protected, please login and enroll course to view this content!
Next
2.5 WHILE Loop