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During this course, we will continue to learn another important application in supervised learning - solving classification problems. In the following lessons, you will be exposed to: logistic regression, K-nearest neighbor algorithm, naive Bayes, support vector machine, perceptron and artificial neural network, decision tree and random forest, and bagging and boosting methods. The course will start with the principle of each of these methods. You are supposed to fully understand the implementat
| Index | Name | Difficulty | Practice |
|---|---|---|---|
| 01 | 📖 🟢 Logistic Regression Classification with Scikit-Lea... | Beginner | Start Lab |
| 02 | 📖 🟢 K Nearest Neighbor Algorithm | Beginner | Start Lab |
| 03 | 📖 🟢 Probabilistic Classification with Naive Bayes | Beginner | Start Lab |
| 04 | 📖 🔵 Implementation of Gaussian Distribution Function a... | Beginner | Start Lab |
| 05 | 📖 🔵 Nonlinear Pattern Recognition Techniques | Beginner | Start Lab |
| 06 | 📖 🔵 Perceptron and Artificial Neural Network | Beginner | Start Lab |
| 07 | 📖 🔵 Train Handwritten Digits Recognition Neural Networ... | Beginner | Start Lab |
| 08 | 📖 🔵 Decision Tree Classification with Python | Beginner | Start Lab |
| 09 | 📖 🔵 Bagging and Boosting Method | Beginner | Start Lab |
| 10 | 📖 🔵 Quickly Select Models with Cross Validation | Beginner | Start Lab |
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