Machine Learning

 

Machine Learning Tutorial – Index

Machine Learning – Introduction

1. What is Machine Learning
2. Applications of ML
3. ML vs DL

Machine Learning – Model

4. What is a Machine Learning Model
5. Steps to build an ML model
6. What is Train/ Test in an ML Model

Machine Learning – Terminologies

7. Inductive vs Deductive Machine Learning
8. Bagging vs Boosting in Machine Learning 
9. Overfitting & Underfitting in Machine Learning
10. Confusion Matrix in Machine Learning
11. Transfer Learning in Machine Learning

Machine Learning – Types

12. Types of Machine Learning.
13. Supervised Machine Learning
14. Unsupervised Machine Learning
15. Semi-Supervised Machine Learning
16. Reinforcement Machine Learning
17. Supervised vs Unsupervised vs Reinforcement Machine Learning

Machine Learning – Classification

18. What is Classification
19. Applications of Classification
20. Types of Classification
21. Decision Tree
22. K-Nearest Neighbors (KNN) Algorithm Tutorial
23. Support Vector Machine
24. Naive Bayes
25. Random Forest

Machine Learning – Regression

26. What is Regression
27. Applications of Regression
28. Types of Regression
29. What is Logistic Regression
30. What is Linear Regression
31. Linear vs Logistic Regression
32. When to choose Classification over Regression

Machine Learning – Clustering

33. What is Clustering
34. Types of Clustering
35. K-Means Clustering

Machine Learning – Other Algorithms

36. Dimensionality Reduction Algorithms
37. Gradient Boosting Algorithm and AdaBoosting Algorithm

Machine Learning – Model Evaluation

38. What is Model Evaluation
39. Model Evaluation Terminologies
40. Confusion Matrix

Model Evaluation Methods

41. Accuracy
42. Precision
43. Recall

Which metric is important

44. FI Score in Machine Learning

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