This document discusses machine learning topics including applications of machine learning, mathematics in machine learning, types of machine learning algorithms, regression algorithms like linear regression and multiple linear regression, classification algorithms like logistic regression and support vector machines, clustering algorithms like k-means clustering, and decision tree algorithms like random forest. It provides an overview of key concepts in machine learning including traditional programming vs machine learning approaches, bias-variance trade-off, dimensionality reduction, confusion matrices, and the elbow method.

1. Machine Learning
Name: Subhadip Mondal
Reg. No.: 11701711
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3. Applications of Machine Learning
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5. Mathematics in Machine Learning
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7. Traditional Programming
Vs.
Machine Learning Approach
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8. Types of Machine Learning
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10. Bias-Variance Trade-Off
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11. Dimensionality Reduction
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12. Types of Regression Algorithms
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13. Linear Regression
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14. Multiple Linear Regression
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15. Polynomial Regression
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16. Types of Classification Algorithms
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17. Confusion Matrix
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18. Logistic Regression
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19. S-Curve
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20. Support Vector Machines
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21. K-Nearest Neighbors (KNN) Classification
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22. Naïve Bayes
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23. Bayes’ Theorem
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24. Decision Tree Algorithms
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25. Decision Tree Terminology
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26. How Does A Tree Decide Where To Split?
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27. Random Forest Algorithms
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28. Why Random Forest?
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30. K-Means Clustering
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31. Elbow Method
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