-
Machine Learning ~ Section 01 : Introduction & Supervised / Unsupervised Learning 4
-
Machine Learning and its Uses and Roles 10 minLecture1.1
-
1.1 Introduction 07 minLecture1.2
-
1.2 Types of Machine Learning 05 minLecture1.3
-
1.3 Different Supervised Learning Algorithms 06 minLecture1.4
-
-
Python ~ Section 01 : Introduction And Getting The Right Tools! 1
-
1.1 Introduction And Installation 05 minLecture2.1
-
-
Python ~ Section 02 : Basic I/O, Operators & Using IDE 5
-
2.1 Numbers and Strings 07 minLecture3.1
-
2.2 Lists and Dictionaries 07 minLecture3.2
-
2.3 Assignment Operators 05 minLecture3.3
-
2.4 Development Environment 04 minLecture3.4
-
2.5 Visual Studio Code: [VS_Code] 07 minLecture3.5
-
-
Python ~ Section 03 : Conditional Statements & Looping ! 5
-
3.1 Conditional Statements 05 minLecture4.1
-
3.2 User Input 05 minLecture4.2
-
3.3 WHILE Loop 05 minLecture4.3
-
3.4 FOR Loop 03 minLecture4.4
-
3.5 FOR Loop: (Dictionary Enumeration) 05 minLecture4.5
-
-
Python ~ Section 04 : OOPS! Functions, Classes & Exception Handling 4
-
4.1 Functions 07 minLecture5.1
-
4.2 Class and Objects 04 minLecture5.2
-
4.3 Constructors 05 minLecture5.3
-
4.4 Exception handling 07 minLecture5.4
-
-
Python ~ Section 05 : Python Modules & Experiencing Jupyter ! 5
-
5.1 Modules 06 minLecture6.1
-
5.2 Statistics Module 04 minLecture6.2
-
5.3 CSV Module 08 minLecture6.3
-
5.4 PIP 04 minLecture6.4
-
5.5 Jupyter Note Book 07 minLecture6.5
-
-
Python ~ Section 06 : Tkinter, SQL in Python & File Management 3
-
6.1 SQLite 10 minLecture7.1
-
6.2 Tkinter 11 minLecture7.2
-
6.3 Making [.exe] in Python 08 minLecture7.3
-
-
[Bonus] Python Hands-On Projects + Source Code 4
-
Rock Paper Scissor Python Game 13 minLecture8.1
-
Message Encode Decode in Python Project 15 minLecture8.2
-
Calculator in Python 25 minLecture8.3
-
Source Code of all 3 ProjectsLecture8.4
-
-
Python Assignments 2
-
Assignment No. 1Lecture9.1
-
Assignment No. 2Lecture9.2
-
-
Machine Learning ~ Section 02 : Regression [Models, Implementation] 13
-
2.1 Linear Regression + Introduction 10 minLecture10.1
-
2.2 Linear Regression Mathematics 13 minLecture10.2
-
2.3 Linear Regression Implementation 13 minLecture10.3
-
2.4 Regression Using Karl Pearsons Coefficient 05 minLecture10.4
-
2.5 Linear Regression using Karlpearson Coefficient Implementation 06 minLecture10.5
-
2.6 Linear Regression Library Implementation 05 minLecture10.6
-
2.7 Loss Analysis Using Mse 08 minLecture10.7
-
2.8 Mean Squared Error (MSE) 04 minLecture10.8
-
2.9 Goodness Of Fit 10 minLecture10.9
-
2.10 R-Squared Implementation 03 minLecture10.10
-
2.11 R Squared Using Karl Pearson Coefficient 05 minLecture10.11
-
2.12 R Squared Using Karl Pearson 05 minLecture10.12
-
2.13 Library Implementation of Metrics 03 minLecture10.13
-
-
Machine Learning ~ Section 03 : Data Processing & Pandas 4
-
3.1 Loss Optimizer 09 minLecture11.1
-
3.2 Gradient Descent Implementation 09 minLecture11.2
-
3.3 Data Processing Using Pandas 11 minLecture11.3
-
3.4 Train Test Split 06 minLecture11.4
-
-
Machine Learning ~ Section 04 : Classification, Score Analysis & [Bonus] Google Colabration 9
-
4.1 Classification Models 08 minLecture12.1
-
4.2 Logistic Regression 05 minLecture12.2
-
4.3 Loss For Classification Models 03 minLecture12.3
-
4.4 Log Loss Implementation 07 minLecture12.4
-
4.5 Score Analysis Basics 05 minLecture12.5
-
4.6 Confusion Matrix Implementation 08 minLecture12.6
-
4.7 Precision and Recall 07 minLecture12.7
-
4.8 F1 Score 05 minLecture12.8
-
[Bonus] Google Collaboration 09 minLecture12.9
-
-
Machine Learning ~ Section 05 : K-Means Clustering, Decision Tree Classifier, Support Vector Information 6
-
5.1 K Nearest neighbors 08 minLecture13.1
-
5.2 Iris 10 minLecture13.2
-
5.3 Support Vector Machine 10 minLecture13.3
-
5.4 Decision Tree Classifier 06 minLecture13.4
-
5.5 Digit Classification 08 minLecture13.5
-
5.6 K Means Clustering 13 minLecture13.6
-
This content is protected, please login and enroll course to view this content!
Prev
3.4 FOR Loop
Next
4.1 Functions