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Python ~ Section 01 : Introduction And Getting The Right Tools! 1
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1.1 Introduction And Installation 05 minLecture1.1
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Python ~ Section 02 : Basic I/O, Operators & Using IDE 5
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2.1 Numbers and Strings 07 minLecture2.1
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2.2 Lists and Dictionaries 07 minLecture2.2
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2.3 Assignment Operators 05 minLecture2.3
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2.4 Development Environment 04 minLecture2.4
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2.5 Visual Studio Code: [VS_Code] 07 minLecture2.5
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Python ~ Section 03 : Conditional Statements & Looping ! 5
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3.1 Conditional Statements 05 minLecture3.1
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3.2 User Input 05 minLecture3.2
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3.3 WHILE Loop 05 minLecture3.3
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3.4 FOR Loop 03 minLecture3.4
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3.5 FOR Loop: (Dictionary Enumeration) 05 minLecture3.5
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Python ~ Section 04 : OOPS! Functions, Classes & Exception Handling 4
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4.1 Functions 07 minLecture4.1
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4.2 Class and Objects 04 minLecture4.2
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4.3 Constructors 05 minLecture4.3
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4.4 Exception handling 07 minLecture4.4
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Python ~ Section 05 : Python Modules & Experiencing Jupyter ! 5
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5.1 Modules 06 minLecture5.1
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5.2 Statistics Module 04 minLecture5.2
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5.3 CSV Module 08 minLecture5.3
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5.4 PIP 04 minLecture5.4
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5.5 Jupyter Note Book 07 minLecture5.5
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Python ~ Section 06 : Tkinter, SQL in Python & File Management 3
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6.1 SQLite Copy 10 minLecture6.1
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6.2 Tkinter Copy 11 minLecture6.2
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6.3 Making [.exe] in Python Copy 08 minLecture6.3
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Web Development ~ Section 01 : Flask Introduction 2
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1.1 Introduction And Installation 02 minLecture7.1
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1.2 Boilerplate Code 05 minLecture7.2
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Web Development ~ Section 02 : Routing & Redirecting 2
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2.1 Routing 07 minLecture8.1
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2.2 Redirecting 07 minLecture8.2
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Web Development ~ Section 03 : Templating, Forms & Making API's With CRUD DB Operations 17
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3.1 Template Prerequisites 12 minLecture9.1
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3.2 Bootstrap 12 minLecture9.2
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3.3 Flask File Hierarchy 06 minLecture9.3
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3.4 Rendering Template 05 minLecture9.4
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3.5 Jinja Templating 05 minLecture9.5
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3.6 Conditions in Jinja 07 minLecture9.6
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3.7 Enumeration In Jinja 04 minLecture9.7
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3.8 Static Directory 06 minLecture9.8
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3.9 Methods 05 minLecture9.9
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3.10 Requests 06 minLecture9.10
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3.11 Flash 10 minLecture9.11
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3.12 Forms-Phase-1 09 minLecture9.12
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3.13 Forms-Phase-2 07 minLecture9.13
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3.14 Forms-Phase-3 10 minLecture9.14
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3.15 Forms-Phase-4 11 minLecture9.15
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3.16 Forms-Phase-5 06 minLecture9.16
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3.17 Forms-Phase-6 12 minLecture9.17
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Machine Learning ~ Section 01 : Introduction & Supervised / Unsupervised Learning 3
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1.1 Introduction 07 minLecture10.1
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1.2 Types of Machine Learning 05 minLecture10.2
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1.3 Different Supervised Learning Algorithms 06 minLecture10.3
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Machine Learning ~ Section 02 : Regression [Models, Implementation] 13
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2.1 Linear Regression Introduction 10 minLecture11.1
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2.2 Linear Regression Mathematics 13 minLecture11.2
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2.3 Linear Regression Implementation 13 minLecture11.3
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2.4 Regression Using Karl Pearson Coefficient 05 minLecture11.4
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2.5 Linear Regression using Karl Pearson Coefficient Implementation 05 minLecture11.5
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2.6 Linear Regression Library Implementation 05 minLecture11.6
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2.7 Loss Analysis Using MSE 08 minLecture11.7
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2.8 Mean Squared Error 04 minLecture11.8
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2.9 Goodness Of Fit 10 minLecture11.9
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2.10 R-Squared Implementation 03 minLecture11.10
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2.11 R-Squared Using Karl Pearson Coefficient 05 minLecture11.11
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2.12 R-Squared Using Karl Pearson 05 minLecture11.12
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2.13 Library Implementation of Metrics 03 minLecture11.13
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Machine Learning ~ Section 03 : Data Processing & Pandas 4
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3.1 Loss Optimizer 09 minLecture12.1
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3.2 Gradient Descent Implementation 09 minLecture12.2
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3.3 Data Processing Using Pandas 11 minLecture12.3
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3.4 Train Test Split 06 minLecture12.4
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Machine Learning ~ Section 04 : Classification, Score Analysis & [Bonus] Google Colabration 9
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4.1 Classification Models 08 minLecture13.1
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4.2 Logistic Regression 05 minLecture13.2
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4.3 Loss For Classification Models 03 minLecture13.3
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4.4 Log Loss Implementation 07 minLecture13.4
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4.5 Score Analysis Basics 05 minLecture13.5
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4.6 Confusion Matrix Implementation 08 minLecture13.6
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4.7 Precision & Recall 07 minLecture13.7
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4.8 F1 Score 05 minLecture13.8
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[Bonus] Google Colabration 09 minLecture13.9
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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 minLecture14.1
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5.2 Iris 10 minLecture14.2
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5.3 Support Vector Machine 10 minLecture14.3
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5.4 Decision Tree Classifier 06 minLecture14.4
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5.5 Digit Classification 08 minLecture14.5
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5.6 K-Means Clustering 13 minLecture14.6
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Hands-On : Capstone Project + Source Code 3
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Phase1 Model-1 26 minLecture15.1
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Phase2 Front-End 15 minLecture15.2
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Phase3 Back-End 18 minLecture15.3
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[Bonus] Python Hands-On Projects + Source Code 4
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Rock Paper Scissor Python Game 13 minLecture16.1
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Message Encode Decode in Python Project 15 minLecture16.2
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Calculator in Python 25 minLecture16.3
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Source Code of all 3 ProjectsLecture16.4
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3.9 Methods