Curriculum
- 5 Sections
- 62 Lessons
- 10 Weeks
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- Week 120
- 2.11.1 Introduction And Installation5 Minutes
- 2.21.2 Numbers and Strings7 Minutes
- 2.31.3 Lists and Dictionaries7 Minutes
- 2.41.4 Assignment Operators5 Minutes
- 2.52.1 Development Environment4 Minutes
- 2.62.2 Visual Studio Code: [VS_Code]7 Minutes
- 2.72.3 Conditional Statements5 Minutes
- 2.82.4 User Input5 Minutes
- 2.92.5 WHILE Loop5 Minutes
- 2.103.1 FOR Loop3 Minutes
- 2.113.2 FOR Loop: (Dictionary Enumeration)5 Minutes
- 2.123.3 Functions7 Minutes
- 2.134.1 Class and Objects4 Minutes
- 2.144.2 Constructors5 Minutes
- 2.155.1 Exception handling7 Minutes
- 2.165.2 Modules6 Minutes
- 2.175.3 Statistics Module4 Minutes
- 2.186.1 CSV Module8 Minutes
- 2.196.2 PIP4 Minutes
- 2.206.3 Jupyter Note Book7 Minutes
- Week 212
- 3.11.1 SQLite10 Minutes
- 3.22.1 Introduction to ML7 Minutes
- 3.32.2 Types of Machine Learning5 Minutes
- 3.42.3 Different Supervised Learning Algorithms6 Minutes
- 3.53.1 Tkinter11 Minutes
- 3.63.2 Making [.exe] in Python8 Minutes
- 3.74.1 Linear Regression + Introduction10 Minutes
- 3.84.2 Linear Regression Mathematics13 Minutes
- 3.94.3 Linear Regression Implementation13 Minutes
- 3.105.1Rock Paper Scissor Python Game13 Minutes
- 3.116.1 Regression Using Karl Pearson Coefficient5 Minutes
- 3.126.2 Linear Regression using Karlpearson Coefficient Implementation
- Week 312
- 4.11.1 Message Encode Decode in Python Project15 Minutes
- 4.22.1 Linear Regression Library Implementation5 Minutes
- 4.32.2 Loss Analysis Using Mse8 Minutes
- 4.42.3 Mean Squared Error4 Minutes
- 4.53.1 Calculator in Python25 Minutes
- 4.64.1 Goodness Of Fit10 Minutes
- 4.74.2 R-Squared Implementation3 Minutes
- 4.85.1 R Squared Using Karl Pearson Coefficient5 Minutes
- 4.95.2 R-Square Dusing Kral pearson5 Minutes
- 4.105.3 Library Implementation of Metrics3 Minutes
- 4.116.1 Loss Optimizer9 Minutes
- 4.126.2 Gradient Descent Implementation9 Minutes
- Week 417
- 5.11.1 Data Processing Using Pandas11 Minutes
- 5.21.2 Train Test Split6 Minutes
- 5.31.3 Classification Models8 Minutes
- 5.42.1 Logistic Regression5 Minutes
- 5.52.2 Loss For Classification Models3 Minutes
- 5.62.3 Log Loss Implementation7 Minutes
- 5.72.4 Score Analysis Basics5 Minutes
- 5.83.1 Confusion Matrix Implementation8 Minutes
- 5.93.2 Precision & Recall
- 5.103.3 F1 Score5 Minutes
- 5.114.1 Google Colabration9 Minutes
- 5.124.2 K Nearest neighbors8 Minutes
- 5.135.1 Iris
- 5.145.2 Support Vector Machine10 Minutes
- 5.155.3 Decision Tree Classifier6 Minutes
- 5.166.1 Digit Classification8 Minutes
- 5.176.2 K Means Clustering13 Minutes
- Python Projects Source Code1
6.2 K Means Clustering
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