Getting Started with Machine Learning

This repository contains all the source codes for ML tutorials available for YouTube.

The list of all the tutorials is given as follows :

1. Machine Learning Types
2. Classification and Regression Difference
3. Linear Regression Mathematically
4. Linear Regression Python Implementation
5. Other ways to implement Linear Regression in Python
6. Mean Squared Error Intuition with Python Implementation
7. R-Squared Intuition and Python Implementation
8. R-Squared without Predictors Intuition & Python Code
9. Gradient Descent Loss Optimizer
10. Gradient Descent Python Implementation from Scratch
11. Logistic Regression
12. Logistic Regression Python Implementation
13. Confusion Matrix Concept and Python Implementation
14. Precision and Recall Concept and Python Implementation
15. F1 Score Concept and Python Implementation
16. Log Loss with Python Implementation
17. Gradient Descent for Logistic Regression with Implementation
18. Pandas Walkthrough
19. Train Test Split
20. Simple Imputation
21. Label Encoding
22. One Hot Encoding
23. Frequent Count Imputation
24. K Nearest Neighbors - Conceptualization and Implementation
25. Type I and Type II Error
26. Sensitivity and Specificity with FPR
27. ROC Curve and AUC
28. Normalization
29. Standardization
30. Decision Trees
31. Entropy
32. Information Gain
33. Gini Impurity
34. Random Forest with Bias Variance Concepts
35. Cross Validation Practical Implementation
36. Grid Search CV
37. AdaBoost
38. Support Vector Machines
39. SVM Kernels

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