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

2.2 Lists and Dictionaries

Lists and Dictionaries

A list is an ordered and mutable Python container, being one of the most common data structures in Python. To create a list, the elements are placed inside square brackets ([]), separated by commas. As shown above, lists can contain elements of different types as well as duplicated elements , where as Dictionary in Python is an ordered collection of data values, used to store data values like a map, which, unlike other Data Types that hold only a single value as an element, Dictionary holds key:value pair. Key-value is provided in the dictionary to make it more optimized. A list is an ordered sequence of objects, whereas dictionaries are unordered sets. However, the main difference is that items in dictionaries are accessed via keys and not via their position. The values of a dictionary can be any type of Python data.

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Prev 2.1 Numbers and Strings

Next 2.3 Assignment Operators

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

2.2 Lists and Dictionaries

Lists and Dictionaries

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