Supervised Learning Algorithmswith better lifecycle.pptx

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1. Supervised Learning Algorithms Step by Step Journey

2. Supervised Learning with Scikit-Learn Classification and Regression: Scikit-learn provides algorithms for supervised learning tasks such as classification, where the goal is to predict discrete class labels, and regression, where the goal is to predict continuous numerical values. Algorithms: Scikit-learn offers a variety of supervised learning algorithms, including support vector machines (SVM), decision trees, random forests, gradient boosting, k-nearest neighbors (KNN), and more. Evaluation and Metrics: Scikit-learn provides tools to evaluate the performance of supervised learning models, including metrics such as accuracy, precision, recall, F1-score, and ROC-AUC curve.

3. Naïve Bayes Classifier

4. Naïve Bayes Classifier

5. Naïve Bayes Classifier

6. Decision Trees Definition: Decision Trees are tree-like structures where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes. They partition the data based on features to make predictions. Usage in Real Time: Decision Trees are used in various domains such as finance for credit scoring, healthcare for medical diagnosis, and marketing for customer segmentation. They are interpretable and can handle both numerical and categorical data.

7. Support Vector Machines (SVM) Definition: Support Vector Machines are supervised learning models used for classification and regression tasks. SVM finds the hyperplane that best separates classes in feature space while maximizing the margin between classes. Usage in Real Time: SVM is widely used in image classification, text classification, and bioinformatics. It is effective in high-dimensional spaces and can handle non-linear data using kernel functions like polynomial, radial basis function (RBF), and sigmoid.

8. Support Vector Machines (SVM)

9. Support Vector Machines (SVM)

10. Support Vector Machines (SVM)

11. Support Vector Machines (SVM)

12. Support Vector Machines (SVM)

13. Support Vector Machines (SVM)

14. Support Vector Machines (SVM)

15. Support Vector Machines (SVM)

16. Random Forest Definition: Random Forest is an ensemble learning method that builds multiple decision trees during training and outputs the mode of the classes for classification tasks or the mean prediction for regression tasks. Usage in Real Time: Random Forest is used in predictive modeling, anomaly detection, and feature selection. It is robust against overfitting and noise, making it suitable for large and complex datasets in finance, healthcare, and e-commerce.

17. K-Nearest Neighbors (KNN) Definition: K-Nearest Neighbors is a non-parametric classification algorithm that assigns a class label based on the majority vote of its k-nearest neighbors in feature space. Usage in Real Time: KNN is used in recommendation systems, pattern recognition, and anomaly detection. It doesn't require training time and adapts well to changes in the data. However, it can be computationally expensive for large datasets.

18. Regression Algorithm in Supervised Learning Definition: Regression is a supervised learning algorithm used for predicting continuous numerical values based on input features. It models the relationship between the independent variables (features) and the dependent variable (target) to make predictions. Usage in Real-Time: Predicting house prices based on features like area, number of bedrooms, and location. Forecasting sales revenue based on factors like advertising spending, seasonality, and economic conditions. Estimating the crop yield based on factors like weather conditions, soil quality, and irrigation methods.

19. Logistic Regression

20. Logistic Regression

21. Logistic Regression

22. Logistic Regression

23. Logistic Regression

24. Logistic Regression

25. Multiple Linear Regression

26. Multiple Linear Regression

27. Multiple Linear Regression

28. Multiple Linear Regression

29. Multiple Linear Regression

30. Multiple Linear Regression

31. Multiple Linear Regression

32. Thanks