23 Sep Machine Learning Tutorial

Machine Learning is a subset of Artificial Intelligence. The computer is fed with data and information. This gives these systems the ability to learn and enhance from experiences. The process happens without being explicitly programmed. In the below figure, you can easily understand the ecosystem of Machine Learning:

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The Machine Learning tutorial is prepared for students, engineers, and professionals. This tutorial will be useful for understanding Machine Learning models.

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. Decision Tree
20. K-Nearest Neighbors (KNN)Algorithm Tutorial
21. Support Vector Machine
22. Naive Bayes
23. Random Forest

Machine Learning – Regression

24. What is Regression
25. What is Logistic Regression
26. What is Linear Regression
27. Linear vs Logistic Regression
28. When to choose Classification over Regression

Machine Learning – Clustering

29. What is Clustering
30. Types of Clustering
31. K-Means Clustering

Machine Learning – Other Algorithms

32. Dimensionality Reduction Algorithms
33. Gradient Boosting Algorithm and AdaBoosting Algorithm

Machine Learning – Scenarios

34. Examples with Answers

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