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