Supervised Learning: Classification

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

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2018-08-19 18:41 Updated
Difficulty: Fundamental Score: 5 22 Learned
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  • Labs
  1. cant
    Lab
    Logistic Regression
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  2. cant
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    K-Nearest Neighbor Algorithm
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  3. cant
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    Naive Bayes
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  4. cant
    Challenge
    Implementation of Gaussian Distribution Function and Draw
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  5. cant
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    Support Vector Machines
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  6. cant
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    Perceptron and Artificial Neural Network
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  7. cant
    Challenge
    Train Handwritten Digits Recognition Neural Network
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  8. cant
    Lab
    Decision Tree
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  9. cant
    Lab
    Bagging and Boosting Method
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  10. cant
    Challenge
    Quickly Select Models with Cross-validation
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Supervised Learning: Classification
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